A signal analysis method and system for a GLONASS new system signal real-time software receiver

Abstract

This invention discloses a signal analysis method and system for a real-time software receiver of the new GLONASS system signal, solving the technical problem that the signal analysis process of existing civilian GLONASS software receivers is difficult to meet the real-time requirements of software receivers in practical applications. The method includes acquiring the new GLONASS system signal and the local PRN code; preprocessing with a Doppler frequency table and a predefined number of sampling points to generate multiple sets of intermediate frequency signals; combining the local PRN code and the coherent integration duration acquisition signal to output the captured Doppler precision estimate; generating a baseband low-frequency signal and six sets of correlation results based on the resampled code library and other tracking signals; outputting code phase observations and five-domain signal quality indicators through signal analysis; outputting a valid navigation message through synchronous decoding such as CRC check; and finally, combining the message, indicators, etc., completing the positioning calculation based on the receiver's initial position approximation to output the receiver's final three-dimensional position.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a signal analysis method and system for a real-time software receiver of a new GLONASS signal system. Background Technology

[0002] GLONASS (Global Navigation Satellite System) is a global satellite navigation system developed by Russia. Together with the US GPS (Global Positioning System), Europe's Galileo (Galileo Satellite Navigation System), and China's BeiDou (BeiDou Navigation Satellite System), it forms the four major global satellite navigation systems. GLONASS operation consists of three segments: space, ground control, and user. The space segment comprises medium Earth orbit (MEO) satellites at an altitude of approximately 19,100 km, an inclination of 64.8°, and a period of approximately 11 hours and 15 minutes. To adapt to the modernization of global navigation systems and further improve its performance and international interoperability, the GLONASS system has gradually begun a technological upgrade process, officially entering a new stage of development.

[0003] As the modernization of global satellite navigation systems continues, the technological upgrade of the GLONASS satellite navigation system has been fully implemented, fundamentally transforming it from the traditional FDMA (Frequency Division Multiple Access) system to the CDMA (Code Division Multiple Access) system. This transformation not only precisely achieves the goals of the previous technological upgrade but also significantly enhances the system's interoperability with mainstream navigation systems such as GPS and Galileo. It also brings about comprehensive and significant optimizations in signal structure, modulation methods, and message formats. The new system's signal performance in terms of anti-interference capability, positioning accuracy, and multi-signal compatibility represents a qualitative leap compared to the old system, laying a core foundation for the civilian promotion and industrial application of the GLONASS system.

[0004] Current civilian GLONASS software receivers still rely on the old-system FDMA signal processing for signal analysis, lacking sufficient support for real-time acquisition, stable tracking, and complete data parsing of the new-system CDMA signals. Especially in complex application scenarios such as concurrent reception at multiple frequencies and compatible switching between multiple signal systems, a standardized framework for software processing of new-system signals has not yet been established, and a unified and standardized technical path has not been developed for signal performance evaluation. These technical shortcomings directly restrict the civilian deployment and large-scale promotion of GLONASS new-system signals, making it difficult to meet the real-time requirements of software receivers in practical applications. Summary of the Invention

[0005] This invention provides a signal analysis method and system for a real-time software receiver of the new GLONASS system, which solves the technical problem that the signal analysis process of existing civilian GLONASS software receivers is difficult to meet the real-time requirements of software receivers in practical applications.

[0006] The first aspect of this invention provides a signal analysis method for a real-time software receiver of a new GLONASS signal system, comprising: The GLONASS new system signal and local PRN code are acquired, and the GLONASS new system signal is preprocessed using a preset Doppler frequency table according to a predefined number of sampling points to generate multiple sets of intermediate frequency signals. Signal acquisition is performed based on the local PRN code, multiple sets of intermediate frequency signals, and a preset coherent integration time, and the captured Doppler precision value, initial code phase, and carrier-to-noise ratio are output. Based on the pre-generated resampled code library, the captured Doppler precision estimate, the initial code phase, the carrier-to-noise ratio, and multiple sets of intermediate frequency signals, signal tracking is performed to generate a baseband low-frequency signal and six sets of correlation results; Based on the six sets of related results, the baseband low-frequency signal and the preset ideal signal parameters, signal analysis is performed to output code phase observations and five-domain signal quality indicators. The signal is synchronously decoded using a preset CRC check rule based on the six sets of related results, a preset second-level code parameter table, a preset convolutional code rate, and a preset frame synchronization header, and a valid navigation message bit stream is output. The receiver positioning is calculated based on the effective navigation message bit stream, the five-domain signal quality index, the code phase observation, and the approximate initial position of the receiver, and the final three-dimensional position of the receiver is output.

[0007] Optionally, the GLONASS new system signal is preprocessed using a preset Doppler frequency table according to a predefined number of sampling points to generate multiple sets of intermediate frequency signals, including: The GLONASS new system signal is converted into 8-bit complex-sampled I / Q data; Calculate the sine and cosine sequences based on the predefined number of sampling points; Based on the sine sequence, the cosine sequence, and the 8-bit complex sampled I / Q data, multiple mixing results are pre-calculated; Based on the multiple mixing results, a carrier mixing table is generated; Based on the preset Doppler frequency table, candidate frequency offset intervals covering the effects of satellite motion and receiver clock bias are divided, and a Doppler candidate frequency offset set is constructed. The 8-bit complex sampled I / Q data is formatted, and the formatted 8-bit complex sampled I / Q data is output. Based on the Doppler candidate frequency offset set and the carrier mixing table, a lookup table mixing operation is performed point by point on the formatted 8-bit complex sampled I / Q data to obtain multiple sets of intermediate frequency signals.

[0008] Optionally, the step of acquiring the signal based on the local PRN code, multiple sets of the intermediate frequency signals, and a preset coherent integration time, and outputting the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio, includes: Perform single-instruction multiple-data-stream parallel multiplication and fast Fourier transform on multiple sets of intermediate frequency signals and local PRN codes to obtain multiple related output matrices; Based on the preset coherent integration time, coherent integration is performed on each of the relevant output matrices to generate multiple I / Q data pairs. The amplitude squares of the multiple I / Q data pairs are calculated and accumulated to obtain a two-dimensional incoherent integrated power map. In the two-dimensional incoherent integral power graph, the maximum correlation peak and the average noise power are searched, and the carrier-to-noise ratio is calculated using the maximum correlation peak and the average noise power. Compare the carrier-to-noise ratio with the preset carrier-to-noise ratio threshold; If the carrier-to-noise ratio is greater than or equal to the preset carrier-to-noise ratio threshold, then extract the Doppler index corresponding to the maximum correlation peak and the Doppler frequencies and incoherent integral amplitudes of the left and right neighboring points of the Doppler index. Using the Doppler frequencies of the left and right neighboring points as independent variables and the corresponding incoherent integral amplitudes as dependent variables, the captured Doppler precision estimate is obtained by least squares polynomial fitting. The initial code phase is obtained by converting the code phase index corresponding to the maximum correlation peak value, the chip length of the local PRN code, and the sampling rate.

[0009] Optionally, the signal tracking based on the pre-generated resampled code library, the captured Doppler precision estimate, the initial code phase, the carrier-to-noise ratio, and multiple sets of the intermediate frequency signals generates a baseband low-frequency signal and six sets of correlation results, including: Based on the captured Doppler precision estimate, the initial code phase, and the carrier-to-noise ratio, the initial state of the tracking loop is determined; Based on the initial state of the tracking loop and the target intermediate frequency signal corresponding to successful acquisition from multiple sets of intermediate frequency signals, a local replica carrier is generated; The target intermediate frequency signal is mixed with the local replicated carrier to obtain the baseband low frequency signal; The lead pseudocode, instantaneous pseudocode, and lag pseudocode in the pre-generated resampling code library are subjected to time-domain correlation operations with the baseband low-frequency signal to obtain six sets of correlation results.

[0010] Optionally, the step of performing signal analysis based on the six sets of correlation results, the baseband low-frequency signal, and preset ideal signal parameters, and outputting code phase observations and five-domain signal quality indicators, includes: The real-time carrier-to-noise ratio is calculated based on the amplitude of the six sets of related results; The real-time carrier-to-noise ratio is compared with a preset lock-and-hold threshold. If the real-time carrier-to-noise ratio is greater than the preset lock-and-hold threshold, then a carrier phase observation and a code phase observation are generated. Based on the carrier phase observations, the code phase observations, and the preset ideal signal parameters, time-domain analysis is performed on the baseband low-frequency signal; frequency-domain analysis is performed on the signal spectrum obtained by Fourier transform of the baseband low-frequency signal; correlation-domain analysis is performed on the code correlation characteristics corresponding to the six sets of correlation results; modulation-domain analysis is performed on the I / Q modulation components obtained by splitting the baseband low-frequency signal; and measurement-domain analysis is performed on the phase relationship between the carrier phase observations and the code phase observations, outputting a five-domain signal quality index.

[0011] Optionally, the step of using a preset CRC check rule to perform signal synchronization decoding based on the six sets of related results, a preset second-level code parameter table, a preset convolutional code rate, and a preset frame synchronization header, and outputting a valid navigation message bit stream, includes: Based on the six sets of related results and the preset secondary code parameter table, a local secondary code is generated; A matching operation is performed between the six sets of related results and the local secondary code to obtain the synchronized secondary code; The six sets of related results and the synchronized binary code are multiplied together to obtain a pure symbol stream; Based on the preset convolutional code rate, the Viterbi algorithm convolutional decoding is performed on the pure symbol stream to restore it to the information bit stream; Based on the preset frame synchronization header and the preset CRC check rule, the frame start position is searched in the information bit stream; Perform CRC check on the entire frame data corresponding to the frame start position and output a valid navigation message bit stream.

[0012] Optionally, the step of performing receiver positioning calculations based on the effective navigation message bit stream, the five-domain signal quality index, the code phase observation, and the receiver's initial position approximation, and outputting the receiver's final three-dimensional position, includes: Identify the string type in the valid navigation message bit stream, and based on the string type, call the bit parsing function to extract the satellite data field; The quantized values ​​corresponding to the satellite data fields are converted into physical quantities, and a standardized ephemeris and time parameter set is output. The orbit extrapolation process is performed on the ephemeris data in the standardized ephemeris and time parameter set to obtain the three-dimensional coordinates of the satellite target at that time. Perform coordinate system one and geometric operations on the three-dimensional coordinates of the satellite target at the time and the approximate value of the receiver's initial position to obtain the geometric distance between the satellite and the receiver, the satellite's azimuth angle and elevation angle; The code phase observation is weighted and corrected by the five-domain signal quality index, the satellite azimuth angle and the elevation angle to obtain the weighted and corrected code phase observation. Based on the three-dimensional coordinates of the satellite target at the specified time, the geometric distance between the satellite and the receiver, and the weighted and corrected code phase observations, multiple sets of pseudorange equations are constructed. Based on the approximate initial position of the receiver, a first-order Taylor expansion linearization process is performed on multiple sets of pseudorange equations to obtain a linear observation equation set. The least squares algorithm is applied to the linear observation equations to obtain the receiver position correction and clock error correction. Based on the receiver position correction amount and the clock error correction amount, the approximate value of the initial position of the receiver is updated to obtain the updated three-dimensional position of the receiver. A threshold comparison is performed between the receiver position correction amount and the clock error correction amount. If both the receiver position correction amount and the clock error correction amount are less than the corresponding preset threshold, then the updated receiver three-dimensional position is taken as the final three-dimensional position of the receiver.

[0013] The second aspect of this invention provides a signal analysis system for a real-time software receiver of a new GLONASS signal system, comprising: The signal preprocessing module is used to acquire the GLONASS new system signal and the local PRN code, and to perform signal preprocessing on the GLONASS new system signal according to a preset Doppler frequency table and a predefined number of sampling points to generate multiple sets of intermediate frequency signals. The signal acquisition module is used to acquire signals based on the local PRN code, multiple sets of intermediate frequency signals, and a preset coherent integration time, and output the captured Doppler precision value, initial code phase, and carrier-to-noise ratio. The signal tracking module is used to track signals based on a pre-generated resampled code library, the captured Doppler precision value, the initial code phase, the carrier-to-noise ratio, and multiple sets of intermediate frequency signals, and generate a baseband low-frequency signal and six sets of correlation results; The signal analysis module is used to perform signal analysis based on the six sets of correlation results, the baseband low-frequency signal, and preset ideal signal parameters, and output code phase observations and five-domain signal quality indicators. The signal synchronization decoding module is used to perform signal synchronization decoding based on the six sets of related results, the preset second-level code parameter table, the preset convolutional code rate, and the preset frame synchronization header using preset CRC check rules, and output a valid navigation message bit stream. The positioning module is used to perform receiver positioning calculations based on the effective navigation message bit stream, the five-domain signal quality index, the code phase observation, and the receiver's initial position approximation, and outputs the receiver's final three-dimensional position.

[0014] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the signal analysis method for the GLONASS new system signal real-time software receiver as described above.

[0015] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the signal analysis method of the real-time software receiver for the GLONASS new system signal as described above.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: The above-described technical solution of the present invention provides a signal analysis method for a real-time software receiver of GLONASS new system signals. The method acquires the GLONASS new system signal and the local PRN code, and preprocesses the GLONASS new system signal using a preset Doppler frequency table based on a predefined number of sampling points to generate multiple sets of intermediate frequency (IF) signals. Signal acquisition is performed based on the local PRN code, the multiple sets of IF signals, and a preset coherence integration time, outputting the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio (CNR). Signal tracking is then performed based on a pre-generated resampled code library, the captured Doppler precision estimate, the initial code phase, the CNR, and the multiple sets of IF signals. The system generates a baseband low-frequency signal and six sets of correlation results; it performs signal analysis based on the six sets of correlation results, the baseband low-frequency signal, and preset ideal signal parameters, outputting code phase observations and five-domain signal quality indicators; it uses preset CRC check rules to perform signal synchronization decoding based on the six sets of correlation results, preset secondary code parameter table, preset convolutional code rate, and preset frame synchronization header, outputting a valid navigation message bit stream; it calculates receiver positioning based on the valid navigation message bit stream, five-domain signal quality indicators, code phase observations, and approximate initial receiver position, outputting the final three-dimensional position of the receiver; based on the above scheme, this invention obtains new GLONASS data. After processing the system signal and local PRN code, the signal is preprocessed using a preset Doppler frequency table and a predefined number of sampling points to generate multiple sets of intermediate frequency (IF) signals, enabling early signal normalization and adaptation. Combining the local PRN code, multiple IF signals, and a preset coherent integration duration, signal acquisition calculations are performed, outputting the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio. This efficiently extracts key signal acquisition parameters without requiring additional complex computational processes, significantly improving signal acquisition speed. Based on a pre-generated resampling code library, signal tracking calculations are performed using the captured output parameters and multiple IF signals to generate a baseband low-frequency signal and six sets of related signals. As a result, pre-configured resources can be fully reused, simplifying the signal matching and processing flow during tracking. Based on six sets of related results, baseband low-frequency signals, and preset ideal signal parameters, signal analysis and calculation are performed, and code phase observations and five-domain signal quality indicators are output. This focuses on core output requirements, avoids redundant data calculation and output, reduces computational load, and improves analysis efficiency. Following preset CRC check rules, combined with six sets of related results and various preset parameters, signal synchronous decoding is performed, and a valid navigation message bit stream is output. Signal synchronous decoding can be completed efficiently according to established specifications, avoiding repeated trial and error in the parsing process and quickly obtaining the navigation message data required for positioning.Finally, by integrating the effective navigation message bitstream, five-domain signal quality indicators, code phase observations, and approximate initial receiver position, positioning calculations are performed, and the final three-dimensional position of the receiver is output. This achieves efficient integration of navigation data and positioning computation, rapidly completing the positioning solution and outputting the result. The synergistic effect of the above steps ensures that the software receiver can quickly complete the entire process from signal acquisition to positioning result output, fully meeting the real-time requirements of practical applications. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the steps of a signal analysis method for a real-time software receiver for a new GLONASS signal system provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the signal acquisition principle provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the signal tracking principle provided in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of digital distortion analysis provided in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the eye diagram model provided in Embodiment 1 of the present invention; Figure 6 This is a schematic diagram of the deviation between the ranging code phase and the carrier phase provided in Embodiment 1 of the present invention; Figure 7 This is a flowchart of signal synchronization decoding provided in Embodiment 1 of the present invention; Figure 8 This is a structural block diagram of a signal analysis system for a real-time software receiver of a new GLONASS signal system, provided in Embodiment 2 of the present invention. Detailed Implementation

[0019] This invention provides a signal analysis method and system for a real-time software receiver of the new GLONASS system, which solves the technical problem that the signal analysis process of existing civilian GLONASS software receivers is difficult to meet the real-time requirements of software receivers in practical applications.

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of the present invention involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations, and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0021] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a signal analysis method for a real-time software receiver of a new GLONASS signal system, as provided in Embodiment 1 of the present invention.

[0022] This invention provides a signal analysis method for a real-time software receiver of a new GLONASS signal system, comprising: Step 101: Obtain the GLONASS new system signal and local PRN code, and use a preset Doppler frequency table to preprocess the GLONASS new system signal according to the predefined number of sampling points to generate multiple sets of intermediate frequency signals.

[0023] The GLONASS new system signal refers to the satellite navigation signal transmitted by the Russian Global Navigation Satellite System (GLONASS) using the new system standard. It is the core received signal of the receiver in this invention and is used for the entire process of subsequent signal preprocessing, acquisition, tracking, analysis and positioning calculation.

[0024] The local PRN code refers to the pseudo-random noise code (PRN code) generated locally by the receiver. It corresponds one-to-one with the PRN code carried in the transmitted signal of the GLONASS new system satellite and is used to perform correlation operations with the intermediate frequency signal to achieve the acquisition and identification of useful signals.

[0025] It should be noted that the GLONASS new system signal and local PRN code are acquired, and the GLONASS new system signal is preprocessed using a preset Doppler frequency table according to a predefined number of sampling points to generate multiple sets of intermediate frequency signals. By matching and adapting preset parameters with a fixed number of sampling points, the pre-conditioning and frequency offset pre-compensation related processing of the signal are quickly completed. The generated multiple sets of intermediate frequency signals can directly provide adaptive input for subsequent signal acquisition operations combined with the local PRN code and preset coherent integration time.

[0026] Further, step 101 may include the following sub-steps: S11. Convert the GLONASS new system signal into 8-bit complex-sampled I / Q data; S12. Calculate the sine and cosine sequences based on the predefined number of sampling points; S13. Based on the sine sequence, cosine sequence, and 8-bit complex sampled I / Q data, pre-calculate multiple mixing results; S14. Generate a carrier mixing table based on multiple mixing results; S15. Based on the preset Doppler frequency table, divide the candidate frequency offset intervals that cover the influence of satellite motion and receiver clock error, and construct a set of candidate Doppler frequency offsets; S16. Format the 8-bit complex sampled I / Q data and output the formatted 8-bit complex sampled I / Q data. S17. Based on the Doppler candidate frequency offset set and carrier mixing table, perform a lookup table mixing operation on the formatted 8-bit complex sampled I / Q data point by point to obtain multiple sets of intermediate frequency signals.

[0027] 8-bit complex-sampled I / Q data refers to the digital signal obtained after the GLONASS new system signal undergoes quadrature down-conversion and 8-bit analog-to-digital conversion. It contains in-phase components (I) and quadrature components (Q), which are used to characterize the amplitude and phase information of the signal and serve as the basic input for subsequent digital signal processing.

[0028] The preset Doppler frequency table refers to a pre-set parameter table that includes the potential frequency offset range caused by satellite motion and receiver clock bias, and is used as the basis for dividing candidate frequency offset intervals.

[0029] The candidate frequency offset interval refers to the frequency offset range that is divided based on a preset Doppler frequency table and covers the influence of satellite motion and receiver clock bias. Each interval corresponds to a candidate frequency offset value.

[0030] The Doppler candidate frequency offset set refers to the set of all candidate frequency offset values, each value corresponding to a potential frequency offset compensation amount, which is used to generate multiple sets of intermediate frequency signals.

[0031] GLONASS (Global Navigation Satellite System) is a global satellite navigation system built and operated by Russia.

[0032] It should be noted that, in order to achieve high-speed, low-latency signal mixing under real-time operating conditions, the receiver employs a carrier lookup table (LUT) pre-calculation mechanism to generate sine and cosine waveform coefficients during the initialization phase, significantly reducing the amount of real-time computation. This is based on the defined number of sampling points. N TBL A sine and cosine sequence for one period is pre-calculated to generate the carrier mixing table. The formula for calculating the carrier lookup table is: (1) After generating the carrier sine and cosine sequences, the program inputs data using 8-bit complex sampling. I, Q Based on this, all possible mixing results are pre-calculated. That is, for each sampling point index... j ∈[0,255] and phase index i ∈[0, N TBL -1], calculate: (2) in, The new in-phase component output after the mixing operation is the result of the original input in-phase component after frequency offset pre-compensation, and is used for the in-phase branch of the subsequent generation of the intermediate frequency signal; The new quadrature component output after the mixing operation is the result of the original input quadrature component after frequency offset pre-compensation, and is related to... The combination forms a pre-compensated complex signal; For the first i Each phase index corresponds to a carrier cosine sequence sample value, which is a point in a pre-generated carrier cosine sequence and is used for lookup table mixing to achieve frequency offset compensation. For the first i Each phase index corresponds to a carrier sinusoidal sequence sample value, which is a point in a pre-generated carrier sinusoidal sequence, and... To complete the complex mixing operation; The results are stored in the carrier table `mix_tbl`. This table is directly accessed during subsequent baseband downconversion, ensuring that the mixing operation for each input sampling point requires only one table lookup, eliminating the need for real-time multiplication and trigonometric operations. This lookup table mechanism implements the "pre-computation for real-time" principle, performing a one-time calculation during initialization and replacing real-time computation with table lookups, significantly reducing CPU computational load.

[0033] In this embodiment, the received GLONASS new system signal is converted into 8-bit complex sampled I / Q data through analog-to-digital conversion and quadrature down-conversion processing, completing the quantization conversion from radio frequency signal to digital baseband signal. Then, based on the predefined number of sampling points, a sine sequence and a cosine sequence matching the sampling rate are generated to construct the carrier waveform basis for frequency offset compensation. Next, these pre-generated sine and cosine sequences are mixed with the 8-bit complex sampled I / Q data to pre-calculate the mixing results under all possible combinations of sampling points and phase points. These results are organized and stored to generate a carrier mixing table. At the same time, based on the preset Doppler frequency table and combined with the influence range of satellite motion characteristics and receiver clock bias, candidate frequency offset intervals covering all potential frequency offsets are divided, thereby constructing a carrier mixing table containing multiple candidate frequency offsets. The Doppler candidate frequency offset set is generated, and then the original 8-bit complex sampled I / Q data is structurally regularized to output formatted 8-bit complex sampled I / Q data, making it directly compatible with the index logic of subsequent lookup table mixing. Finally, the carrier mixing table is indexed by each candidate frequency offset value in the Doppler candidate frequency offset set, and the lookup table mixing operation is performed point by point on the formatted 8-bit complex sampled I / Q data. Each candidate frequency offset corresponds to the generation of an intermediate frequency signal that has undergone frequency offset pre-compensation, thus obtaining multiple sets of intermediate frequency signals. This process generates a carrier mixing table by pre-calculating the mixing results, replacing the multiplication and trigonometric operations in real-time processing with lookup table operations, which significantly reduces the CPU computing load. At the same time, the pre-construction of the Doppler candidate frequency offset set achieves accurate frequency offset coverage, effectively improving the efficiency of subsequent signal acquisition.

[0034] Step 102: Acquire the signal based on the local PRN code, multiple intermediate frequency signals, and the preset coherent integration time, and output the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio.

[0035] It should be noted that the local PRN code is correlated with multiple sets of intermediate frequency signals, and the signal energy is accumulated by combining the preset coherent integration time. The frequency offset and code phase are determined by detecting the correlation peak, and then the captured Doppler precision estimate is obtained by fitting, the initial code phase is converted, and the output carrier-to-noise ratio is calculated to provide accurate initial parameters for subsequent signal tracking.

[0036] Furthermore, step 102 may include the following sub-steps: S21. Perform single-instruction multiple-data-stream parallel multiplication and fast Fourier transform on multiple sets of intermediate frequency signals and local PRN codes to obtain multiple related output matrices; S22. Based on the preset coherent integration time, perform coherent integration on each relevant output matrix to generate multiple I / Q data pairs, and calculate the square of the amplitude of multiple I / Q data pairs and accumulate them to obtain a two-dimensional incoherent integrated power map. S23. In the two-dimensional incoherent integral power graph, search for the maximum correlation peak and the average noise power, and use the maximum correlation peak and the average noise power to calculate the carrier-to-noise ratio. S24. Compare the carrier-to-noise ratio with the preset carrier-to-noise ratio threshold; S25. If the carrier-to-noise ratio is greater than or equal to the preset carrier-to-noise ratio threshold, then extract the Doppler index corresponding to the maximum correlation peak and the Doppler frequencies and incoherent integral amplitudes of the left and right neighboring points of the Doppler index. S26. Using the Doppler frequencies of the left and right neighboring points as independent variables and the corresponding incoherent integral amplitudes as dependent variables, the captured Doppler precision estimate is obtained by least squares polynomial fitting. S27. Convert the code phase index corresponding to the maximum correlation peak value, the chip length of the local PRN code, and the sampling rate to obtain the initial code phase.

[0037] The correlation output matrix refers to the matrix data that represents the correlation degree corresponding to different code phases and different frequency offsets after multiple sets of intermediate frequency signals and local PRN codes are multiplied and Fourier transformed. It is the basis for subsequent integration operations.

[0038] It should be noted that the main function of the signal acquisition module is to search for the code phase and Doppler frequency offset of the target satellite signal in the preprocessed intermediate frequency sampling data, and to determine whether a valid signal exists. The principle is as follows: Figure 2 As shown, the Doppler search range is first constructed based on a preset Doppler frequency table or external auxiliary information. Then, the input signal is mixed with a locally generated sine and cosine table to compensate for candidate frequency offsets. The correlation output under different code phases is then rapidly calculated using a parallel correlation method based on Fast Fourier Transform (FFT). During this process, the amplitude squares of the correlation results are calculated and accumulated to obtain a two-dimensional search power map of "code delay × Doppler frequency," which represents the correlation result. i and q The time is Data pairs are generated after coherent integration. I and Q Finally, the amplitude of the incoherent integral is obtained after incoherent integration. P After obtaining the incoherent integral amplitude of the two-dimensional distribution, the algorithm searches for the maximum correlation peak and estimates the carrier-to-noise ratio by combining it with the average noise power. C / N 0: (3) in, and These are the maximum and average values ​​of the incoherent integral amplitude, respectively. It is the spreading code period. If C / N If the threshold is exceeded, the capture is considered successful, and calculation is performed. Corresponding Doppler index dop_ix and code phase index codephase_ix Then in dop_ix Three sets of Doppler estimation results and their incoherent integral amplitudes are obtained by taking one neighboring point on each side. A precise Doppler estimate is then obtained using least-squares polynomial fitting. After successful acquisition, the receiver locks the corresponding channel and sets the precise Doppler estimate, code phase, and... C / N Output 0 to the tracking module to proceed with the subsequent tracking and navigation message demodulation process; if the decision condition is not met, the channel returns to the idle state and waits for the next round of acquisition.

[0039] It is worth mentioning that, to meet the real-time requirements of the software receiver, Single Instruction Multiple Data (SIMD) instructions are used to accelerate carrier mixing and local PRN code correlation operations. SIMD is a CPU instruction set that allows the same arithmetic operation to be applied to multiple data segments simultaneously within a single instruction cycle, enhancing the CPU's parallel computing capabilities. It is well-suited for processing large amounts of data through simple, uniform operations. For example, the acquisition correlator in the receiver is implemented by a simple multiplication operation between digitized I / Q samples and locally generated PRN codes. This operation can be efficiently parallelized using SIMD. Figure 3 As shown, four sets of I / Q samples are packed into a SIMD register, while the corresponding PRN code copy values ​​are loaded into another register. Then, a single SIMD instruction performs parallel multiplication on all data pairs.

[0040] The GLONASS new system software receiver of this invention adopts Intel's second-generation Advanced Vector Extensions 2 (AVX2) SIMD instruction set. AVX2 supports 256-bit wide vector operations and can process eight 32-bit or four 64-bit data elements simultaneously in a single instruction, realizing parallel dot product operations of two sets of vectors. Its theoretical computational efficiency is approximately eight times that of scalar operations. Because the data preprocessing section merges a set of IQ sample data into a uint8 byte, AVX2 (Advanced Vector Extensions 2) can be used to process eight bytes of data in parallel, i.e., eight sets of IQ sample data. This vectorized parallel mechanism is not only applicable to GLONASS (Global Navigation Satellite System, Russia) software receivers, but can also be extended to the high-performance implementation of multi-system GNSS (Global Navigation Satellite System) receivers such as GPS (Global Positioning System, USA), BDS (Beidou Navigation Satellite System, China), and Galileo (Galileo Satellite Navigation System, EU), providing a unified parallel optimization framework for multi-constellation and multi-frequency signal processing.

[0041] In this embodiment, a single-instruction multiple-data-stream parallel multiplication mechanism is used to synchronously perform multiplication operations between multiple sets of intermediate frequency signals and local PRN codes. Then, a Fast Fourier Transform (FFT) is uniformly performed on the results to convert the time-domain signals into frequency-domain signals, thereby obtaining multiple correlation output matrices representing the corresponding relationships of different code phases and frequency offsets. Based on a preset coherent integration duration, each correlation output matrix is ​​accumulated and integrated along its corresponding dimension to accumulate useful signal energy and suppress noise interference, generating multiple I / Q data pairs containing in-phase and quadrature components. Subsequently, the squared amplitude value is calculated for each I / Q data pair and accumulated, constructing a two-dimensional incoherent integral power map with code delay as the horizontal axis and Doppler frequency as the vertical axis. In this two-dimensional incoherent integral power map, the maximum correlation peak and its location information are located using a peak detection algorithm. Simultaneously, the average noise power is obtained by statistically analyzing the average energy of the noise region. The carrier-to-noise ratio (CNR) is calculated using the ratio of the maximum correlation peak to the average noise power. The calculated CNR is then compared with a preset... The carrier-to-noise ratio (CNR) is compared to a preset CNR threshold to determine if the useful signal has been successfully captured. If the CNR is less than the preset CNR threshold, the capture is considered a failure, and the signal capture process is restarted. If the CNR is greater than or equal to the preset CNR threshold, the capture is considered successful. At this point, the corresponding candidate frequency offset in the candidate frequency offset set is matched according to the Doppler index corresponding to the maximum correlation peak. Then, the signal corresponding to the candidate frequency offset is determined from multiple sets of intermediate frequency signals as the target intermediate frequency signal that has been successfully captured. Subsequently, the Doppler index corresponding to the maximum correlation peak and the Doppler frequencies and incoherent integral amplitudes corresponding to the left and right neighboring points are extracted. A polynomial fitting model is constructed with the Doppler frequencies of these left and right neighboring points as independent variables and the corresponding incoherent integral amplitudes as dependent variables. The model parameters are solved by the least squares algorithm to obtain a high-precision Doppler estimate (i.e., the captured Doppler estimate). Finally, the initial code phase is obtained by performing a delay conversion based on the code phase index corresponding to the maximum correlation peak and the ratio of the chip length to the sampling rate of the local PRN code. This process improves data processing speed through parallel computing, enhances signal anti-interference capability by combining coherent and incoherent integration, improves the judgment process for successful and failed acquisition, and clarifies the method for determining the target intermediate frequency signal. It quickly completes signal acquisition, target signal positioning, and core parameter extraction, providing accurate initial input and adaptability signals for subsequent signal tracking, and effectively improves the efficiency and reliability of receiver signal processing.

[0042] Step 103: Based on the pre-generated resampled code library, the captured Doppler precision estimate, the initial code phase, the carrier-to-noise ratio, and multiple sets of intermediate frequency signals, perform signal tracking to generate the baseband low-frequency signal and six sets of correlation results.

[0043] It should be noted that, based on the target intermediate frequency signal determined in the acquisition stage, the tracking loop is initialized by combining the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio. The corresponding pseudo-code in the pre-generated resampled code library is called to perform carrier frequency and code phase tracking on the target intermediate frequency signal. The baseband low-frequency signal is generated through mixing and down-conversion processing. At the same time, the three pseudo-codes are correlated with the baseband low-frequency signal to generate six sets of correlation results, providing stable input data for subsequent signal analysis and signal synchronous decoding.

[0044] Furthermore, step 103 may include the following sub-steps: S31. Determine the initial state of the tracking loop based on the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio; S32. Generate a local replica carrier based on the initial state of the tracking loop and the target intermediate frequency signal corresponding to successful acquisition from multiple sets of intermediate frequency signals; S33. Perform a mixing operation between the target intermediate frequency signal and the local replicated carrier to obtain the baseband low frequency signal; S34. Perform time-domain correlation operations on the lead pseudocode, instantaneous pseudocode, and lag pseudocode in the pre-generated resampling code library and the baseband low-frequency signal to obtain six sets of correlation results.

[0045] Tracking loop initial state: refers to the initial working state of the tracking loop after calibration based on the capture output parameters. It includes core configuration information such as initial carrier frequency, initial code phase, and loop filtering parameters, and is the basis for ensuring stable signal tracking.

[0046] The locally replicated carrier refers to the carrier signal generated by the receiver through a numerically controlled oscillator based on the initial state of the tracking loop and the characteristics of the target intermediate frequency signal. This carrier signal is adapted to the carrier frequency and phase of the target intermediate frequency signal and is used to mix with the target intermediate frequency signal to remove the carrier frequency component.

[0047] A leading pseudocode refers to a pseudocode in a pre-generated resampled code library whose phase leads the ideal pseudocode by one chip interval (or half a chip interval). It is used to correlate with the baseband low-frequency signal to detect the phase lead deviation of the code.

[0048] Instantaneous pseudocode refers to a pseudocode in a pre-generated resampled code library whose phase perfectly matches that of the ideal pseudocode. It is used to correlate with the baseband low-frequency signal to accurately lock the code phase.

[0049] Lag pseudocode refers to a pseudocode in the pre-generated resampled code library whose phase lags behind the ideal pseudocode by one chip interval (or half a chip interval). It is used to correlate with the baseband low-frequency signal to detect code phase lag deviation.

[0050] It should be noted that after signal acquisition, the software receiver enters the signal tracking phase. The main task of the signal tracking module is to continuously refine the initial Doppler frequency offset and code phase obtained in the acquisition phase to maintain coherent locking to the satellite signal and output steady-state carrier and code observations in real time. This module adopts a joint working mechanism of code loop (Delay Lock Loop, DLL) and carrier loop (Phase Lock Loop / Frequency Lock Loop, PLL / FLL) to dynamically update the code phase, carrier phase, and frequency compensation in each processing cycle to maintain phase synchronization between the local signal and the received signal.

[0051] Furthermore, the basic principle diagram of the satellite navigation receiver tracking loop is as follows: Figure 3 As shown. In the receiver, numbered... i intermediate frequency signal of the satellite Generally expressed in complex form: (4) in, It is the I-channel intermediate frequency signal and the Q-channel signal. The phase difference between the I-channel signal and the I-channel signal is 90°. Taking the I-channel signal as an example, the specific form of the signal is as follows: (5) in, It is the total gain of the RF front end. and These are the pseudo-random code and navigation message bits modulated onto the carrier wave, respectively. It is the nominal frequency of the intermediate frequency signal. The Doppler frequency shift is caused by the motion of the satellite and carrier, as well as the receiver clock bias. It is the initial phase of the carrier signal; Intermediate frequency noise (IF) is additive white Gaussian noise introduced into signal transmission in space and in the receiver's radio frequency link, which can interfere with the detection and processing of useful signals.

[0052] The mixer's function is to mix the locally replicated signal generated by the voltage-controlled oscillator (VCO) with the intermediate frequency (IF) signal, thereby stripping the carrier. For ease of derivation, the pseudo-random code and navigation message bits are omitted, and the IF signal... and locally replicated carrier The mathematical forms can be expressed as follows: (6) (7) in, , This refers to the angular frequency of the intermediate frequency signal; For a moment in time; The amplitude of the locally replicated carrier signal reflects the strength of the locally generated carrier and needs to be matched with the amplitude of the input intermediate frequency signal to ensure the mixing effect. The angular frequency of the locally replicated carrier is generated by the receiver based on the captured Doppler precision estimate, and is used to track and match the angular frequency of the input intermediate frequency signal; The initial phase of the locally replicated carrier is set by the receiver based on the acquisition results. It is used to match the initial phase of the input intermediate frequency signal and improve mixing efficiency. After passing through the mixer, we can obtain: (8) in, The output signal of the mixer is the direct result of mixing the input intermediate frequency signal with the local replicated carrier. It contains two components: high frequency and low frequency. After the high frequency component is filtered out by a filter, the remaining low frequency component will be used for phase error identification. It is a key intermediate signal for phase locking in signal tracking. The high frequency signal in the first half of the formula is filtered out after passing through the subsequent filter, leaving only the low frequency signal close to 0 for phase error identification.

[0053] In the code loop section, to ensure precise phase alignment between the local pseudocode and the received signal, the software receiver needs to generate local pseudo-random code copies matching the sampling rate. This system employs a resampled code bank to pre-generate multiple sets of local code copies with different code phases, balancing high accuracy and real-time performance. During the receiver program initialization phase, the program allocates code bank storage space for each satellite tracking channel, with a length equal to the number of sampling points N in the code period multiplied by the number of code copies N_CODES. Subsequently, the sdr_res_code() function is called repeatedly to pre-generate N_CODES sets of code copies with different phases, with each code copy having an initial phase difference of... The system achieves sub-sampling level code phase interpolation by generating several code copies of adjacent phases (e.g., N_CODES=10), significantly improving code ring resolution. Pre-generated code copies are directly invoked during tracking, eliminating the need for real-time resampling, greatly reducing CPU load and ensuring real-time performance. Each code copy is stored contiguously in memory and can be directly matched with the baseband signal via FFT or time-domain correlation to achieve multi-delay parallel matching, further enhancing tracking performance.

[0054] Signal after carrier stripping The three pseudo-random code signals, namely lead (E), instantaneous (P), and lag (L), are copied locally. Perform relevant calculations and generate relevant outputs. Here, we take the instantaneous local code of the I-branch as an example, and the relevant operations are as follows: (9) in, The normalized correlation output between the local pseudocode and the signal after carrier stripping reflects the phase matching degree between the local pseudocode and the received signal pseudocode, and is the core parameter for code phase tracking. For delay n The instantaneous local pseudocode sequence after each sampling point is a discrete sampled value of the locally generated instantaneous pseudocode after a time delay, which is used to perform correlation operations with the input signal to detect code phase error. The discrete sampled signal after carrier stripping is a low-frequency signal output from the mixer and filtered, which serves as one of the input signals for correlation operations. N This is the number of sampled data points equal to the code period length. Let: (10) (11) in, The difference between the intermediate frequency signal angular frequency and the local replicated carrier angular frequency, i.e., the angular frequency error, reflects the magnitude of the carrier frequency tracking deviation and is used for subsequent carrier frequency error correction. The phase error is the difference between the initial phase of the intermediate frequency signal and the initial phase of the locally replicated carrier. It reflects the magnitude of the phase tracking deviation and is used for subsequent phase error identification and correction. The signal expression after correlation operation can be written as: (12) in, The instantaneous correlation output of the I branch is the in-phase correlation result between the instantaneous local pseudocode and the signal after carrier stripping, and is the core data for realizing code phase tracking and signal synchronous decoding. It is the code phase difference between the locally copied pseudo-random code and the received pseudo-random code. It is the pseudocode autocorrelation function with respect to the pseudocode phase error, when When it is 0, It has a maximum value of 1. Similarly, the I / Q components of the signal after carrier stripping (i.e., the baseband low-frequency signal) and Correlation with the three local codes (i.e., the leading pseudocode, the immediate pseudocode, and the lagging pseudocode in the pre-generated resampled code library) yields six sets of correlation results: , , , , , ; , , , , , These are the outputs of the in-phase (I) and quadrature (Q) correlations between the three pseudocodes (lead, instant, and lag) and the carrier-stripped signal.

[0055] It is worth mentioning that the carrier loop consists of an FLL / PLL (Frequency Lock Loop / Phase Lock Loop) phase detector, a loop filter, and a carrier NCO (Numerically Controlled Oscillator). The main function of the discriminator is to identify the tracking error of the signal. The loop filter filters the discriminator result, and the filtered result serves as the control signal for the carrier and code generator to control the generation of the locally replicated signal. A frequency-locked loop (FLL) is used during the tracking pull-in period to improve robustness, and then switches to a phase-locked loop (PLL) after steady state. The phase error is calculated using a Costas loop or an arctangent phase detector. After loop filtering, the carrier NCO is controlled to update the local carrier frequency and phase, thereby achieving phase tracking and Doppler drift compensation.

[0056] (13)

[0057] The code ring section uses a normalized lead-lag discriminator to estimate the code phase error. The local code rate is adjusted according to the error, thereby locking the pseudocode at the center of the signal correlation peak.

[0058] (14)

[0059] The integrator-crusher primarily performs low-pass filtering on the signal through integration to remove high-frequency signals generated in the mixer. It is assumed that after correlation, the spurious signals in the signal have been completely removed. From time 10:00, the signal has been in transit for a duration of 100 seconds. After coherent integration, we can obtain: (15) Coherent integration can improve the signal-to-noise ratio (SNR) of a signal. The ability of a receiver to track weak low-Earth orbit (LEO) satellite navigation signals largely benefits from the SNR gains brought by coherent integration. During tracking, the receiver calculates carrier-code coherence and instantaneous... C / N 0 and locked state. When the signal C / N When 0 is above the threshold, the system maintains a stable lock; when C / N When the value is below the threshold, the signal is considered lost and the channel is reset to an idle state.

[0060] in, It is an instantaneous estimate of the carrier phase error, obtained through arctangent operation, reflecting the phase deviation between the locally replicated carrier and the received signal carrier, and is the core basis for carrier loop phase tracking and correction; For the instantaneous correlation output of the Q branch; For the instantaneous correlation output of branch I; The estimated value of the code phase error is obtained by the ratio of the correlation energy difference and energy sum of the leading and lagging branches. It reflects the phase deviation between the local pseudocode and the received signal pseudocode and is used for code phase tracking and correction in the code loop. , These are the in-phase and quadrature correlation outputs of the leading branch, respectively, which are used in conjunction with the results of the lagging branch to calculate the code phase error; , These are the in-phase and quadrature correlation outputs of the lagging branch, respectively, used for calculating the code phase error; The output signal of the I branch after coherent integration is the result of low-pass filtering of the correlation output by the integrator-clearer, which is used to improve the signal-to-noise ratio and enhance the tracking ability of weak signals. C / N 0 represents the carrier-to-noise ratio.

[0061] In this embodiment, the initial carrier frequency parameters of the tracking loop are set based on the Doppler precision estimate output from the acquisition stage. The initial phase reference of the code tracking branch is configured using the initial code phase, and the initial signal quality is evaluated in conjunction with the carrier-to-noise ratio to match the loop filtering parameters. These three parameters are combined to complete the calibration and determination of the initial state of the tracking loop. Subsequently, based on the calibrated initial state of the tracking loop (including core parameters such as initial carrier frequency and initial phase), and combined with the frequency and phase characteristics of the target intermediate frequency signal corresponding to successful acquisition from multiple sets of intermediate frequency signals, a numerically controlled oscillator (NCO) is used. A locally replicated carrier with precise frequency and phase matching to the target intermediate frequency (IF) signal is generated. The target IF signal and the locally replicated carrier are input to a mixer module for multiplication. High-frequency components generated after mixing are filtered out by a low-pass filter, retaining the useful low-frequency signal to obtain the baseband low-frequency signal. Finally, lead pseudo-code, instantaneous pseudo-code, and lag pseudo-code are retrieved from a pre-generated resampling code library. The three pseudo-codes are then subjected to time-domain sliding correlation with the baseband low-frequency signal. The in-phase (I) and quadrature (Q) components of each pseudo-code after correlation with the baseband signal are simultaneously acquired, and six sets of correlation results are output. This process achieves stable tracking of the target IF signal by accurately initializing the tracking loop state, efficiently generating the locally replicated carrier, and quickly completing the correlation operation, significantly reducing signal tracking delay and bit error rate.

[0062] Step 104: Perform signal analysis based on the six sets of related results, the baseband low-frequency signal, and the preset ideal signal parameters, and output the code phase observation and the five-domain signal quality index.

[0063] Code phase observations refer to the observational data obtained through signal analysis that characterize the phase matching between the local pseudocode and the received signal pseudocode, and are one of the core observations for positioning calculation.

[0064] Five-domain signal quality index refers to a set of indicators that evaluate signal quality from five core dimensions (such as amplitude domain, phase domain, frequency domain, time domain, and signal-to-noise ratio domain). It is used to intuitively characterize the stability, reliability, and anti-interference ability of the received signal.

[0065] The preset ideal signal parameters refer to the ideal characteristic parameters (including ideal amplitude, ideal phase, standard frequency, etc.) that are preset by the receiver and conform to the GLONASS new system signal standard, serving as the benchmark for comparison and analysis with the actual received signal parameters.

[0066] It should be noted that the code phase deviation information is extracted based on the six sets of related results output by the signal tracking stage. Combined with the amplitude and phase characteristics of the baseband low-frequency signal, it is compared and analyzed with the preset ideal signal parameters to accurately calculate the code phase observation. At the same time, the stability and reliability of the signal are evaluated from multiple dimensions, and the five-domain signal quality index is output to provide core data support for subsequent signal synchronization decoding and positioning calculation.

[0067] Furthermore, step 104 may include the following sub-steps: S41. Calculate the real-time carrier-to-noise ratio based on the amplitude of the six sets of related results; S42. Compare the real-time carrier-to-noise ratio with the preset lock-and-hold threshold; S43. If the real-time carrier-to-noise ratio is greater than the preset lock-and-hold threshold, then generate carrier phase observations and code phase observations. S44. Based on carrier phase observations, code phase observations, and preset ideal signal parameters, perform time-domain analysis on the baseband low-frequency signal, frequency-domain analysis on the signal spectrum obtained by Fourier transform of the baseband low-frequency signal, correlation-domain analysis on the code correlation characteristics corresponding to the six sets of correlation results, modulation-domain analysis on the I / Q modulation components obtained by splitting the baseband low-frequency signal, and measurement-domain analysis on the phase relationship between carrier phase observations and code phase observations, and output five-domain signal quality indicators.

[0068] Real-time carrier-to-noise ratio (CNR) refers to the ratio of carrier power to noise power calculated in real time based on the amplitude values ​​of six sets of correlation results. It is used to dynamically reflect the quality of the currently received signal and the working stability of the tracking loop.

[0069] The preset lock-in maintenance threshold refers to the carrier-to-noise ratio critical value pre-configured by the receiver to determine whether the signal maintains a stable lock-in state, serving as a benchmark parameter for signal lock-in state discrimination.

[0070] Carrier phase observations refer to the observational data obtained after calibration based on the estimated carrier phase error of the tracking loop and the Doppler compensation under stable signal locking conditions. These observations characterize the phase deviation between the locally replicated carrier and the received signal carrier and are one of the core observations for navigation and positioning calculations.

[0071] Code phase observation refers to the observation data obtained by calculating the code phase deviation through the difference of the correlation results of the three paths of lead / lag / instant under the signal stable locking state, combined with the initial code phase reference calibration. It characterizes the phase deviation between the locally copied pseudocode and the received signal pseudocode and is one of the core observations for navigation and positioning calculation.

[0072] Time-domain analysis refers to the analysis of the waveform, amplitude, distortion, and fluctuation characteristics of baseband low-frequency signals with time as the independent variable. It is used to identify the distortion and interference characteristics of signals in the time dimension.

[0073] Frequency domain analysis refers to the analysis of the signal spectrum of a baseband low-frequency signal after Fourier transform. It is used to detect abnormal frequency domain characteristics such as signal frequency shift, noise floor, and spectral leakage.

[0074] Correlation domain analysis refers to the analysis of code correlation characteristics corresponding to six sets of correlation results, used to evaluate correlation peak morphology, sidelobe suppression effect and code phase matching accuracy.

[0075] Modulation domain analysis refers to the analysis of I / Q modulation components obtained by splitting the baseband low-frequency signal, which is used to detect the phase orthogonality and amplitude uniformity of the quadrature modulation components.

[0076] Measurement domain analysis refers to the analysis of the phase relationship and jitter characteristics of carrier phase observations and code phase observations, which is used to quantify the accuracy and stability of the observations.

[0077] It should be noted that, based on signal tracking, the receiver further extracts intermediate correlation results and tracking measurement results such as carrier and pseudocode to strip the message data code from the original signal, thereby obtaining purer pseudocode or carrier components. This data can be directly used for subsequent signal quality analysis, including time-domain waveform analysis, correlation function generation, modulation domain eye diagram plotting, and carrier constellation diagram analysis, generating standardized analysis reports that provide intuitive and quantifiable evidence for system performance evaluation, signal modeling, and algorithm verification. Through this integrated signal processing and analysis design, the system achieves a full-link processing flow from raw sampling to quality evaluation, possessing both real-time performance and meeting the accuracy requirements of GLONASS new system signal analysis.

[0078] The platform's built-in generalized signal quality analysis module can perform a comprehensive quality assessment of GLONASS new system signals, covering five dimensions: time domain, frequency domain, correlation domain, modulation domain, and measurement domain.

[0079] Furthermore, regarding the time-domain analysis of the signal: 1) Time-domain waveform distortion The intermediate frequency (IF) carrier signal is removed based on the tracking results to obtain the baseband waveform. Noise in the baseband signal can affect the chip's edge overshoot and duty cycle; therefore, the baseband signal must be coherently accumulated and averaged to minimize the impact of noise on the chip waveform. A schematic diagram of digital distortion is shown below. Figure 4 As shown.

[0080] Chip duty cycle calculation involves statistically analyzing sampling points at the zero-crossing points of the chip, performing linear fitting on points near the zero-crossing points of the rising and falling edges of the chip, and obtaining the zero-crossing points of the two fitted edges. The time interval between the two zero points is the chip duration. The duration corresponding to each positive and negative chip within the code period is statistically analyzed, and the difference is calculated with the ideal chip length to obtain the time difference sequences between "1" and "0" chips and the ideal chip. The maximum, minimum, and peak-to-peak values ​​of the two time sequences are then calculated, along with their standard deviation and mean.

[0081] 2) Eye diagram

[0082] An eye diagram is defined as the synchronous overlap of all possible values ​​of a signal of interest (e.g., the received signal, the receiver output) observed within a specific signal interval. In the analysis of measured data, the acquired data undergoes filtering, demodulation, and Doppler shift removal processes. Starting from the initial phase, multiple chip data are repeatedly plotted in the time-domain waveform. To illustrate the relationship between the eye diagram and system performance, the eye diagram can be simplified as follows: Figure 5 The eye diagram model shown.

[0083] Eye diagrams provide a wealth of useful information about digital communication systems: The optimal sampling time should be when the "eye" is wide open. The slope of the eye diagram determines the sensitivity to timing errors; the steeper the slope, the more sensitive it is to timing errors. At the sampling time, the vertical height of the upper and lower branches of the "eye" indicates the degree of noise interference to the signal at the sampling time, which is the noise tolerance of the system; when the instantaneous noise value exceeds the noise tolerance, erroneous judgment may occur.

[0084] The horizontal axis position in the center of the eye diagram corresponds to the decision threshold level; the range of variation in the zero point position has a significant impact on the extraction of timing information.

[0085] When intersymbol interference is severe, traces from the upper part of the eye diagram will intersect with traces from the lower part, resulting in the "eye" closing completely and severe system errors.

[0086] Furthermore, regarding the frequency domain analysis of the signal: 1) Power spectral analysis The Welch method is used to estimate the power spectrum. The principle is to divide the original sequence into M overlapping segments, smooth the power spectrum of each segment by applying a window, and then average the power spectra of each segment to obtain the power spectrum estimate for the entire sequence. Let the received satellite navigation signal be x(n). Divide it into N segments and apply a window to each segment. Then the DFT (Discrete Fourier Transform) of each segment is: (16) Then, taking the square of its amplitude-frequency response and dividing it by N, we can obtain the spectral estimate using the Welch periodogram method: (17) in, For the first k The discrete sampled signal after segmentation and windowing is the time-domain data of the original satellite navigation signal after segmentation and windowing, which is used as the input for DFT operation; For the first k The Discrete Fourier Transform (DFT) result of the windowed data segment is a frequency domain signal that reflects the frequency components and amplitude distribution of the data segment. Normalized angular frequency; For the first k Temporal sampling index of segment data; The length of each data segment is the number of sampling points in each segment after the original signal is divided, and the length of the DFT operation is also the length of the DFT operation. The first period obtained by Welch periodogram method k The power spectral density estimate of a segment of data reflects the power distribution of that segment of signal at different frequencies and is the core output of power spectrum estimation. 2) Analysis of deviations in the synthesized power spectrum In modern navigation signal systems, each frequency point contains multiple signal components. To ensure a constant envelope for multiple components at a single frequency point, various multiplexing techniques are typically used to synthesize these components. Therefore, if any signal component exhibits an anomaly, the power spectrum of the synthesized signal will also change accordingly. Based on this, the difference between the power spectrum of the synthesized signal and the power spectrum of the ideal signal—the synthesized power spectrum deviation—can be used to reflect anomalies in the navigation signal.

[0087] First, calculate the power spectrum of the ideal signal, ensuring its resolution matches that of the actual signal. Subtract the two and calculate the mean residual within the transmission bandwidth. Then, adjust the amplitude of the ideal signal's power spectrum to match that of the actual signal.

[0088] Furthermore, for correlation domain analysis of the signal: 1) Relevant Loss Analysis The correlation loss reflects the actual power loss of the signal. First, the acquired signal is processed by capturing and tracking to remove the modulated carrier, thus obtaining the baseband signal. The cross-correlation function between the baseband signal and the baseband signal is calculated using the local reference code, and then normalized. The corresponding actual power value is then calculated. The difference between the ideal power and the actual power is the correlation loss.

[0089] Let the locally generated reference signal be The actual received acquisition signal is Code period Let be the integration time. Then the cross-correlation function between the actual received signal and the locally generated reference signal is: (18) The cross-correlation is normalized, and the normalization factor is... The product of the ideal signal power and the received signal power can be expressed as a radical: (19) Thus, the normalized cross-correlation function is obtained as follows: (20) As can be seen from the above formula, the normalized correlation peak is essentially... and The correlation coefficient is calculated. The correlation power of the actual received signal is calculated by solving for the maximum value of the cross-correlation function. The correlation power is then converted into a [formula missing]. The unit is [unit]. Therefore, the relevant power calculation formula is: ;(twenty one) Therefore, the formula for calculating the relevant loss is: ;(twenty two) in, This is the unnormalized cross-correlation function between the actual received signal and the local reference signal; For the relevant points duration; Normalization factor; The code period is the time length corresponding to one complete period of the pseudo-random code. Here, it is used as the integration time for cross-correlation operations, and the length of the integration interval is defined. This is the normalized cross-correlation function; For relevant power; For time delay The normalized cross-correlation function value at time; For related losses; For ideal correlated power; This refers to the actual power. 2) Analysis of the zero-crossing deviation and slope of the S-curve The code loop phase detection curve of a receiver is generally referred to as the S-curve. Ideally, the zero-crossing point of this curve should be where the code tracking error is zero. However, in reality, factors such as channel transmission distortion and noise can cause deviations in the code loop locking point. In practical applications, a noncoherent lead-hysteresis power phase detector is generally used. Assume its lead-hysteresis correlator interval is... Therefore, the S-curve can be expressed as: ;(twenty three) The lock point deviation of the code ring phase detector, i.e. ,satisfy Plot the phase detection curve of the received signal and the lock point deviation. With advance reduction of hysteresis distance The change curve. The S-curve of the signal is obtained under the transmit bandwidth or main lobe bandwidth. Then, the slope of the S-curve at the zero-crossing point is defined as: ;(twenty four) The slope of the zero-crossing point of the ideal S-curve of the simulated signal is determined under different correlator intervals, and the actual slope of the acquired signal is also determined. The slope deviation is calculated by dividing the actual slope by the ideal slope, as follows: (25) in, The phase detection curve (S-curve) of the code ring phase detector represents the code phase error. and lead-lag correlator interval The function reflects the correspondence between code phase error and phase detection output, and is the core characteristic curve of code ring tracking; For lead-lag correlator intervals; For code phase error,; The code ring phase detector's lock point deviation, i.e. the code phase error corresponding to the actual S-curve zero-crossing point, reflects the tracking deviation caused by factors such as channel distortion and noise, and is an indicator for evaluating code ring tracking accuracy. Let S be the slope of the S-curve at the zero-crossing point, and let be the correlator interval. The function reflects the sensitivity of the phase detector to code phase error; the larger the slope, the higher the phase detection sensitivity. The slope deviation rate (expressed as a percentage) is the relative deviation between the actual slope at the zero-crossing point of the S-curve and the ideal slope, used to evaluate the code loop tracking performance loss of the actual signal. The slope of the zero-crossing point of the actual S-curve corresponding to the acquired signal reflects the phase detection sensitivity of the actual signal. The slope of the zero-crossing point of the ideal S-curve corresponding to the simulated signal is used as a benchmark value for evaluating the actual slope. Furthermore, regarding the modulation domain analysis of the signal: 1) Zodiac Chart Analysis Constellation diagrams visually reflect the modulation scheme and distortion level of received navigation satellite signals. After the satellite navigation signal enters the software receiver for acquisition and tracking, the output baseband I / Q modulation components are stripped of the carrier. The baseband signal can be represented as a multiplexed signal of the I / Q branch signals: ,in Cosine signal Amplitude modulation, sinusoidal signal Amplitude modulation. Due to and The phases are orthogonal, therefore and Orthogonal. Usually... These are called the same-direction components. As orthogonal components, respectively with and Using the horizontal and vertical axes, signal constellation diagrams and their transition trajectories can be drawn. Constellation diagrams represent digital signals in the complex plane, providing a visual representation of the relationships between signals.

[0090] 2) Carrier phase deviation analysis

[0091] By using a software receiver to receive and process the radio frequency acquisition signal, the carrier phase deviation value of each signal component is calculated. By comparing it with the ideal carrier phase deviation value, the actual carrier phase deviation value between the signal components can be obtained.

[0092] By subtracting the estimated phase values ​​of the quadrature component carrier from the receiver output and the in-phase component, we can obtain: (26) in, This represents the carrier phase difference between different signal components. It is achieved using the ideal carrier phase and... Subtracting the values ​​yields the carrier phase deviation values ​​for different signal components. Averaging these deviations across multiple points provides the following: (27) in, This is the estimated carrier phase value of the first signal component, reflecting the dynamic change of the carrier phase of this component over time; This is the carrier phase estimate of the second signal component, corresponding to the first component, used to calculate the phase difference between the two components; The nominal angular frequency of the carrier wave; The estimated Doppler angular frequency of the first component; The estimated Doppler angular frequency of the second component corresponds to the estimated Doppler frequency shift of the second component; This is the initial phase estimate of the carrier wave for the first component. This is the initial phase estimate of the carrier for the second component; This represents the estimated phase deviation of the first component; This represents the estimated phase deviation of the second component; The average carrier phase deviation value is obtained by averaging the phase deviations of multiple sampling points. This improves the stability and reliability of phase deviation estimation. For the first i The carrier phase deviation value calculated from each sampling point; n To reduce the number of sampling points involved in the averaging, the impact of noise on phase deviation estimation is reduced by increasing the number of samples.

[0093] Furthermore, for the measurement domain analysis of the signal: 1) Code and carrier phase consistency analysis Code and carrier phase consistency refers to the phase of the ranging code and carrier within the same signal component. Since both are generated using the same clock, the phase relationship between the ranging code and carrier should be fixed. First, the acquired signal is preprocessed. Then, based on the signal's frequency domain structure, bandpass filtering is performed to separate the effective signal, which is then sent to a software receiver for signal acquisition processing. The initial phase of the received signal code and the initial phase of the carrier are estimated, and then estimated values ​​of the code phase and carrier phase are obtained through tracking calculations. The timing deviation is obtained by subtracting these two values. Statistical analysis of the deviation over a period of time is performed to calculate its mean and standard deviation. The timing deviation and root mean square error are used to analyze the consistency between the code and carrier. Figure 6 As shown, That is, the deviation between the ranging code phase and the carrier phase.

[0094] 2) Inter-symbol phase consistency analysis

[0095] Inter-code phase consistency primarily describes the relative time delay deviation between ranging codes in satellite signals. When navigation signals are generated, the starting point of the pseudo-code period theoretically uses a unified time reference. However, in practice, various errors can cause delays, resulting in deviations in the code segments of each signal component. Therefore, it is necessary to analyze the phase consistency between codes to promptly detect positioning errors caused by these deviations. Inter-code phase consistency is divided into intra-frequency and inter-frequency cases.

[0096] a. Intra-frequency code phase consistency

[0097] In satellite navigation signals, orthogonal and in-phase components at the same frequency share the same carrier frequency and are transmitted to the ground via the same path. Therefore, the errors in pseudorange and other observables caused by ionospheric interference during propagation are approximately the same. Based on this, the code phase consistency analysis between different signal components at the same frequency can be directly performed by calculating the pseudorange difference to analyze the consistency between the signals.

[0098] (28)

[0099] in, and These represent the pseudorange of the ranging code for different signal components at the same frequency point; for and The difference.

[0100] b. Inter-frequency code phase consistency

[0101] For navigation signals modulated at different frequencies, they are broadcast using different carrier frequencies. The ionosphere will cause different time delays in signal transmission. In this case, ionosphere removal should be performed on each pseudorange measurement first.

[0102] (29)

[0103] in, This represents the pseudocode measurement value before ionospheric errors were eliminated, while This represents the measured value of the eliminated pseudocode. and These represent different carrier center frequencies. Indicates the carrier wavelength. This represents the carrier phase ranging value converted to distance.

[0104] Based on the above, the amplitudes of the correlation outputs of each branch in the six sets of correlation results are first synchronously sampled and statistically analyzed to separate the effective signal amplitude from the noise floor amplitude. The real-time carrier-to-noise ratio (CNR) is calculated using the ratio of signal amplitude to noise amplitude. The calculated CNR is then compared with a preset lock-and-hold threshold pre-configured within the receiver to determine the current signal tracking stability. When the real-time CNR is greater than the preset lock-and-hold threshold, the signal is considered to be in a stable locked state without risk of loss of lock. Carrier phase calibration is then performed based on the estimated carrier phase error and Doppler compensation in the tracking loop. The code phase deviation is calculated by combining the initial code phase with the difference between the lead / lag / instantaneous correlation results. After normalization, carrier phase observations and code phase observations are generated. Then, using these two types of observations as the basic data and preset ideal signal parameters as the unified evaluation benchmark, the waveform, amplitude fluctuations, and distortion characteristics of the baseband low-frequency signal are evaluated sequentially. The system employs time-domain analysis, frequency-domain analysis of the peak positions, noise floor, and frequency offset characteristics of the signal spectrum obtained from the Fourier transform of the baseband low-frequency signal, correlation-domain analysis of the correlation peak amplitude, peak width, and sidelobe suppression characteristics corresponding to six sets of correlation results, modulation-domain analysis of the phase orthogonality and amplitude equalization of the I / Q modulation components obtained from the decomposition of the baseband low-frequency signal, and measurement-domain analysis of the phase synchronization deviation and dynamic jitter characteristics of the carrier phase observations and code phase observations. Finally, it integrates the quantization results of the five types of analysis to output five-domain signal quality indicators. When the real-time carrier-to-noise ratio is less than or equal to the preset lock-on maintenance threshold, it is determined that the signal is at risk of losing lock or has already lost lock. A lock-on warning signal is immediately generated, the generation process of carrier phase observations and code phase observations is suspended, and the tracking loop is recalibrated (adjusting parameters such as carrier replication frequency and code phase reference) or the signal reacquisition process is triggered to ensure the effectiveness of subsequent signal tracking and analysis. This invention simplifies the signal quality judgment process by directly converting the amplitude of relevant results into the carrier-to-noise ratio, refines the observation generation process to improve data accuracy, and takes into account emergency handling of loss-of-lock scenarios. It adopts a five-domain joint analysis to cover the full-dimensional characteristics of the signal, which not only improves the real-time performance of signal quality assessment, observation calculation and loss-of-lock response, but also comprehensively quantifies signal distortion, interference and tracking errors. It directly improves the shortcomings of traditional receivers in signal analysis, such as single dimension, lagging assessment and untimely loss-of-lock response, and effectively alleviates the technical problems of insufficient real-time performance, incomplete quality characterization and poor data reliability in the signal analysis process.

[0105] Among them, the five-domain signal quality index refers to a multi-dimensional signal quality assessment index obtained by integrating the analysis results of five categories: time domain, frequency domain, correlation domain, modulation domain, and measurement domain. It can comprehensively characterize the distortion, interference, and tracking error levels of the received signal. Each domain corresponds to a quantitative assessment index extracted from the baseband low-frequency signal, signal spectrum, code correlation characteristics, I / Q modulation components, and phase relationship of phase observations. The specific indexes in each domain are calculated based on preset ideal signal parameters and include the following: Time-domain metrics: Quantitative metrics for extracting time-domain features of baseband low-frequency signals. The core metrics include the amplitude fluctuation coefficient, signal distortion rate, and time-domain signal-to-noise ratio of the baseband low-frequency signal, which respectively characterize the amplitude stability, waveform distortion, and separation of effective signal and noise of the baseband low-frequency signal in the time dimension. Frequency domain metrics: Quantitative metrics for extracting the spectrum of a baseband low-frequency signal obtained by Fourier transform. The core metrics include signal frequency offset, spectral noise floor, spectral peak purity, and spectral leakage coefficient, which respectively characterize the degree of frequency offset, frequency domain noise level, energy concentration of spectral peaks, and frequency leakage during the spectrum analysis process. Correlation domain metrics: Quantitative metrics extracted from the code correlation characteristics corresponding to the six sets of correlation results. The core metrics include correlation peak value, correlation peak width, sidelobe suppression ratio, and correlation loss (CL[dB]), which respectively characterize the signal matching degree of code correlation operation, the sharpness of the correlation peak, the anti-interference ability of pseudocode correlation, and the degree of signal energy loss in the actual correlation process. Modulation domain metrics: Quantitative metrics extracted from the I / Q modulation components obtained by splitting the baseband low-frequency signal. The core metrics include I / Q amplitude imbalance, I / Q phase quadrature deviation, and I / Q branch signal-to-noise ratio, which respectively characterize the amplitude consistency and phase quadrature of the I / Q components, as well as the effective signal-to-noise ratio of each component. Measurement domain indicators: Quantitative indicators extracted from the phase relationship of carrier phase observations and code phase observations. The core indicators include carrier phase observation jitter variance, code phase observation jitter variance, carrier-code phase synchronization deviation, and observation accuracy error, which respectively characterize the numerical stability of the two types of observations, the degree of synchronization matching between carrier phase and code phase, and the degree of deviation between actual observations and ideal values.

[0106] Step 105: Using preset CRC check rules, based on six sets of related results, preset secondary code parameter table, preset convolutional code rate, and preset frame synchronization header, perform signal synchronization decoding and output a valid navigation message bit stream.

[0107] CRC check rules refer to pre-defined cyclic redundancy check rules used to verify the integrity and correctness of navigation message data and identify errors in the data transmission or parsing process.

[0108] The Level 2 code parameter table refers to a pre-configured set of parameters that stores the parameters required for Level 2 code decoding (such as code pattern, period, etc.), providing a configuration basis for Level 2 code decoding.

[0109] The convolutional code rate refers to the pre-set encoding rate of the convolutional code, which is used to determine the ratio of information bits to check bits in the deconvolution process. It is the core parameter for message deconvolution.

[0110] The frame synchronization header is a specific bit sequence in the navigation message frame used to identify the start position of the frame, which is used to quickly locate the start of the message frame and ensure the accuracy of parsing.

[0111] It should be noted that, based on the preset frame synchronization header, the starting position of the navigation message frame is located from six sets of related results. The decoding parameters are configured in combination with the preset second-level code parameter table. The message data is deconvolved according to the preset convolution code rate. Then, the integrity and correctness of the processed data are checked by the preset CRC (Cyclic Redundancy Check) check rule. Invalid data is removed to complete the signal synchronization decoding and output a valid navigation message bit stream.

[0112] Furthermore, step 105 may include the following sub-steps: S51. Generate a local secondary code based on the six sets of relevant results and the preset secondary code parameter table; S52. Perform a matching operation between the six sets of related results and the local secondary code to obtain the synchronized secondary code; S53. Multiply the six sets of related results and the synchronized binary code to obtain a pure symbol stream; S54. Based on the preset convolutional code rate, perform Viterbi algorithm convolutional decoding on the pure symbol stream to restore it to the information bit stream; S55. Based on the preset frame synchronization header and the preset CRC check rule, search for the frame start position in the information bit stream; S56. Perform CRC check on the entire frame data corresponding to the frame start position and output the valid navigation message bit stream.

[0113] It should be noted that the L1, L2, and L3 signals in the new GLONASS system all contain a primary code and a secondary code for ranging. After tracking and locking, in order to obtain the original navigation bit message, it is necessary to demodulate the tracking correlation results using the secondary code. First, the matching between the tracking result and the local secondary code is detected through correlation operations. When it is determined that alignment has been achieved, the synchronization time and polarity are recorded. If the signal amplitude is found to be too weak or no longer meets the correlation conditions during the synchronization process, synchronization is considered lost and the state is reset to zero. If synchronization is successfully maintained, the receiver will calculate the corresponding secondary code symbol based on the current lock count and multiply it with the current loop correlation output to eliminate the influence of the secondary code. This receiver supports secondary code synchronization for L1OCd, L2OCp, and L3OCd signals, and the specific parameters are shown in Table 1.

[0114] Table 1. Specific parameters of L1OCd, L2OCp, and L3OCd signals.

[0115] like Figure 7 As shown, after removing the secondary code, frame synchronization and decoding of the navigation message are required. Taking GLONASSL1OCd as an example, the implementation method is as follows: First, a continuous symbol stream (usually 552 soft decision symbols) is collected, and the two outputs of the convolutional code are rearranged to obtain a bit sequence suitable for decoding input. Then, the Viterbi algorithm is used to perform 1 / 2 rate convolutional decoding on the symbol stream, restoring the 552 input symbols to 270 information bits, including 250 bits of valid navigation data. Subsequently, a fixed preamble (the 12-bit synchronization header of the GLONASSL1OCD message) is searched in the decoded bit stream. Once a match is found, the start position of the frame is determined, and the need to reverse the entire frame data is determined based on the bit polarity determination result. If frame synchronization is successful, the data is handed over to the message decoding module for CRC check and subframe data extraction; if the preamble is not detected or the check fails, frame synchronization is considered not established, and the receiver continues to search in the subsequent symbol stream.

[0116] Based on the above, this invention first extracts the code phase features and signal synchronization information from six sets of correlation results. Combining these with core parameters such as the secondary code type, period, and phase offset stored in a preset secondary code parameter table, a local secondary code is generated using a local pseudocode generation module. Subsequently, a sliding correlation matching operation is performed between the six sets of correlation results and the local secondary code. The synchronization position of the secondary code is located based on the peak matching degree, resulting in a synchronized secondary code. The synchronized secondary code is then multiplied bit-by-bit with the data components corresponding to the six sets of correlation results to cancel out the secondary code modulation effect and filter out irrelevant interference components, obtaining a clean symbol stream. Finally, based on a preset convolutional code rate, the following steps are performed: The invention employs a progressive processing approach involving two-level code synchronization, convolutional decoding, and CRC verification. This approach ensures the accuracy and integrity of signal synchronization decoding, effectively improving decoding efficiency and reducing the error rate. Using a pre-defined frame synchronization header as a template, a sliding match is performed within the restored information bitstream to accurately search for and locate the frame start position. The entire frame corresponding to the frame start position is extracted, and a data check value is calculated according to a pre-defined CRC check rule. This check value is compared with the pre-defined check bits carried in the data. If the check passes, a valid navigation message bitstream is output; otherwise, it is marked as invalid and discarded. This invention, through a progressive processing of two-level code synchronization, convolutional decoding, and CRC verification, ensures the accuracy and integrity of signal synchronization decoding, effectively improving decoding efficiency and reducing the error rate.

[0117] Step 106: Calculate receiver positioning based on effective navigation message bit stream, five-domain signal quality index, code phase observation, and approximate initial receiver position, and output the final three-dimensional position of the receiver.

[0118] It should be noted that the core positioning parameters such as satellite ephemeris and satellite clock deviation are extracted from the effective navigation message bit stream. Combined with the five-domain signal quality index, code phase observations that meet the signal quality standards are selected. Based on the selected code phase observations, the pseudorange between the satellite and the receiver is calculated. The approximate value of the receiver's initial position is used as the benchmark for iterative calculation. The pseudorange positioning model is substituted into the model for error correction and iterative solution, and finally the final three-dimensional position of the receiver is output.

[0119] Furthermore, step 106 may include the following sub-steps: S61. Identify the string type in the valid navigation message bit stream, and based on the string type, call the bit parsing function to extract the satellite data field; S62. Convert the quantized values ​​corresponding to the satellite data fields into physical quantities and output a standardized ephemeris and time parameter set; S63. Perform orbit extrapolation processing on the ephemeris data in the standardized ephemeris and time parameter set to obtain the three-dimensional coordinates of the satellite target at the time. S64. Perform coordinate system one and geometric operations on the three-dimensional coordinates of the satellite target at the time and the approximate value of the receiver's initial position to obtain the geometric distance between the satellite and the receiver, the satellite's azimuth angle and elevation angle; S65. The code phase observations are weighted and corrected by using the five-domain signal quality index, satellite azimuth angle and elevation angle to obtain the weighted and corrected code phase observations. S66. Based on the three-dimensional coordinates of the satellite target at the time, the geometric distance between the satellite and the receiver, and the weighted and corrected code phase observations, construct multiple sets of pseudorange equations. S67. Based on the approximate initial position of the receiver, perform first-order Taylor expansion linearization on multiple sets of pseudorange equations to obtain a set of linear observation equations. S68. Solve the linear observation equations using the least squares algorithm to obtain the receiver position correction and clock error correction. S69. Based on the receiver position correction and clock error correction, update the approximate value of the receiver's initial position to obtain the updated three-dimensional position of the receiver. S610. Perform a threshold comparison between the receiver position correction and the clock error correction. If both the receiver position correction and the clock error correction are less than the corresponding preset threshold, then the updated receiver three-dimensional position is taken as the final three-dimensional position of the receiver.

[0120] It should be noted that after completing the second-level code synchronization, frame synchronization, and convolutional decoding, the receiver enters the navigation message parsing stage. The task of this stage is to extract satellite ephemeris parameters, clock bias information, and UTC (Universal Time offset) deviations from the decoded raw navigation data bits for subsequent satellite position and time calculations. The L2OCp signal does not carry navigation data and does not contain ephemeris, clock bias, or time system parameters; this software receiver only acquires the message ephemeris parameters of the L1OCd and L3OCd signals.

[0121] According to ICD-GLONASS-CDMA-L1 and ICD-GLONASS-CDMA-L3, L1OCd and L3OCd messages consist of multiple strings (String Type 10–12, 16, 20, 25, 31, 32, etc.), each corresponding to a specific type of data content, such as satellite ephemeris, clock bias information, Earth rotation parameters, and ionospheric correction models. The receiver first determines its category based on the Type field in the string; for example, Type 10–12 is real-time ephemeris and clock data, Type 20 is an ephemeris summary (Almanac), Type 25 is used for Earth rotation and ionospheric model parameters, while Types 16, 31, and 32 belong to extended or spare information types. For ephemeris strings, the program extracts each field using the bit parsing functions getbitu() and getbitg(), and performs quantization conversion based on the scaling factor defined in the ICD to obtain parameters such as the satellite's three-dimensional coordinates, velocity and acceleration, satellite clock error, epoch time, health status, and signal type identifier in the Earth-fixed coordinate system.

[0122] Furthermore, GLONASS satellite position calculation is based on broadcast ephemeris, and the satellite's spatial position at the target time is obtained through orbital dynamics extrapolation. Unlike systems such as GPS that use Kepler parameters, GLONASS broadcast ephemeris directly provides the satellite's three-dimensional position, velocity, and perturbation acceleration at the reference time, which makes satellite position calculation essentially an orbital state propagation problem.

[0123] In the specific calculations, the receiver first selects the ephemeris record closest to the current observation or transmission time from the navigation message, which is still valid. Then, it calculates the time difference between the target time and the ephemeris reference time, using the position and velocity provided in the ephemeris as the initial state. In a geocentric coordinate system, considering factors such as Earth's gravity, the Earth's non-spherical shape, and Earth's rotation, a dynamic model of the satellite's motion is established. The satellite's orbital state is then extrapolated from the reference time to the target time using numerical integration. This process can employ numerical integration algorithms with fixed or adaptive step sizes to obtain the satellite's three-dimensional coordinates and velocity at the target time.

[0124] Simultaneously with orbit extrapolation, satellite clock bias is calculated based on clock bias parameters from the ephemeris data to correct for signal propagation time. After completing the orbit and clock bias calculations, the satellite position is uniformly represented in a geocentric-geocentric coordinate system. By combining this position with the receiver's position in the same coordinate system, the geometric distance between the satellite and the receiver, as well as the line-of-sight direction, can be calculated.

[0125] To describe the spatial visibility and geometric distribution of satellites, it is usually necessary to transform the line of sight to the local horizontal coordinate system of the receiver's location, thereby obtaining the satellite's azimuth and elevation angles. Azimuth and elevation angles are not only used to determine satellite availability, but also frequently serve as important bases for observation weighting, error modeling, and signal quality analysis.

[0126] Specifically, by using preset string type recognition rules (based on field identifier bits, data length, and encoding format characteristics), the string type corresponding to each segment of data in the valid navigation message bit stream is identified. Then, based on the identified string type, the corresponding bit parsing function is called to extract satellite data fields such as satellite ephemeris, clock deviation, and UTC deviation according to field offset and length. Subsequently, combined with existing physical quantity unit conversion factors and conversion formulas, the quantized values ​​corresponding to the extracted satellite data fields are converted into physical quantities (such as distance, time, and angle units) that conform to navigation and positioning standards, outputting a standardized ephemeris and time parameter set containing standardized ephemeris data and time correction parameters. Based on the ephemeris data in this parameter set, a preset orbit extrapolation model (such as the SDP4 model (Simplified Deep-space Perturbations 4)) is used to perform orbit calculation and deviation correction by substituting the target time, obtaining the three-dimensional coordinates of the satellite at the target time. Finally, the three-dimensional coordinates of the satellite at the target time and the approximate value of the receiver's initial position are uniformly converted to a preset navigation coordinate system (such as the WGS84 coordinate system (World Geodetic System 1984)). The system uses a global geodetic coordinate system (GDC) to calculate the straight-line distance (geometric distance) between the satellite and the receiver using spatial geometric calculation formulas. It then combines this with the coordinate system's azimuth reference to calculate the satellite's azimuth and elevation angles relative to the receiver. This process ensures the accuracy of satellite data extraction and the reliability of geometric parameter calculations through precise string type identification, standardized physical quantity conversion, and efficient orbit extrapolation and coordinate calculations, thereby improving the efficiency and accuracy of subsequent positioning calculations.

[0127] Furthermore, after calculating the satellite position and clock bias, the receiver can perform position calculation based on pseudorange observations. The basic idea of ​​point positioning is to estimate the receiver's three-dimensional position and clock bias by using geometric distance observations from multiple satellites to the receiver and solving simultaneous pseudorange equations.

[0128] For any visible GLONASS satellite, the pseudorange measured by the receiver includes the true geometric distance between the satellite and the receiver, as well as unknowns such as the receiver clock bias. When at least four satellites are available, four independent pseudorange equations can be established, corresponding to four unknown parameters: the receiver's three-dimensional coordinates and the receiver clock bias. Since the pseudorange equations are inherently nonlinear and difficult to solve directly, linearization and iterative methods are typically used in practical receivers.

[0129] In practice, the receiver first provides an initial approximation of its position and clock bias. This initial value can be any coarse estimate, such as the result from the vicinity of the Earth's center or the previous epoch. Then, the pseudorange equation is linearized in the vicinity of this approximation point, transforming the nonlinear distance relationship into a linear observation equation. In the linearized equation, the unknowns represent the corrections between the current position and the approximate position.

[0130] By combining the linearized observation equations from all available satellites, an overdetermined system of equations can be formed. When there are more than four visible satellites, the system contains redundant observations. Receivers typically use least squares estimation to solve the equations, thereby obtaining the statistically optimal position and clock error corrections. By adding the corrections to the original approximate solution, the receiver's position and clock error estimates can be updated.

[0131] The above process typically requires multiple iterations. As the iterations proceed, the approximate position gradually approaches the true position, the correction amount gradually decreases, and finally converges within a predetermined threshold, yielding the receiver's single-point positioning solution. During the iteration process, the observation residuals of each satellite can be used to evaluate the solution quality and provide a basis for subsequent observation weighting, anomaly detection, or signal quality analysis.

[0132] Specifically, the reliability of each code phase observation is first quantitatively evaluated based on the five-domain signal quality index (higher signal quality, higher weight). Weighting coefficients for each observation are then set based on the satellite azimuth and elevation angles (higher elevation angle and less obstruction, higher weight). Error correction is performed on the original code phase observations through weighted calculations, eliminating abnormal deviation components to obtain the weighted corrected code phase observations. Based on the satellite target's three-dimensional coordinates at the time, the geometric distance between the satellite and the receiver, and the pseudorange corresponding to the weighted corrected code phase observations (including receiver clock bias), a set of pseudorange equations is constructed for each visible satellite, forming multiple sets of pseudorange equations. Using the receiver's initial approximate position as a reference point, a first-order Taylor expansion is performed on the multiple sets of pseudorange equations to eliminate nonlinear terms and achieve linearization, resulting in a set of linear observation equations containing unknown receiver position and clock bias. The most... The less-squares algorithm solves the linear observation equations for optimal solutions, minimizing the sum of squared residuals between the observed and calculated values, and outputs receiver position correction and clock error correction. The obtained receiver position correction is then superimposed on the initial receiver position approximation, and combined with the clock error correction to compensate for time deviations, completing the position update and obtaining the updated receiver 3D position. Subsequently, the receiver position correction and clock error correction are compared with their respective preset thresholds. If both are less than the corresponding preset threshold (indicating that the position and clock error have converged to an accurate range), the updated receiver 3D position is taken as the final receiver 3D position. If either correction is not less than the corresponding preset threshold, the current updated receiver 3D position is used as the new initial position approximation, and the above weighted correction, equation construction, linearization, solution, and update steps are repeated until the correction meets the threshold requirements. The above process improves the reliability of the observations through multi-dimensional weighted correction, ensures the accuracy of the solution through linearization and least squares solution, and achieves convergence control by combining threshold judgment. This effectively improves the accuracy and stability of receiver positioning and solves the technical problems of large impact of observation errors, slow solution convergence and large deviation of positioning results in traditional positioning process.

[0133] As a comparison of technical effects, existing technologies can be used as a reference. As the GLONASS satellite navigation system enters the new system (CDMA) stage, its signal structure, modulation method and message format have changed significantly compared with the traditional FDMA system. Most existing civilian GLONASS software receivers are still at the level of old system signal processing and lack the ability to acquire, track and analyze new system signals in real time. In particular, there is no systematic solution for software calculation and performance evaluation under multi-frequency and multi-signal systems.

[0134] Furthermore, while existing open-source software receivers provide general signal sampling, acquisition, tracking, and message decoding modules, they have shortcomings in the following aspects: First, insufficient signal system compatibility. Most software receivers only support older FDMA / GLONASS SSL1 / L2 system signals and do not support parsing the newer CDMA L1OC, L2OC, and L3OC signals. Second, limited real-time performance. Traditional software receivers are primarily post-processing-based and are not optimized for multi-channel parallelism and millisecond-level clock synchronization, making it difficult to achieve efficient real-time signal tracking and parsing. Third, incomplete data links. Existing software receiver platforms mostly remain at the acquisition and tracking stage, lacking a complete implementation from baseband sampling to navigation message parsing, ephemeris extraction, and signal quality analysis. Fourth, a lack of real-time spatial signal quality analysis capabilities. Currently, most software receivers do not support real-time analysis of spatial signal quality, failing to reflect the spectral characteristics, code synchronization performance, multipath effects, and message reliability of signals in real propagation environments. They also cannot verify inter-system compatibility and long-term stability. These limitations prevent existing platforms from being directly used for real-time verification and performance evaluation of GLONASS new system signals, and they also cannot provide complete spatial signal quality monitoring capabilities.

[0135] This invention addresses the aforementioned problems by focusing on solving key challenges in the following three technical directions: First, it proposes a software receiver architecture for GLONASS new system signals, constructing a universal software receiver framework supporting multiple frequency points such as L1OC, L2OC, and L3OC. This framework enables full-process digital processing from acquisition, tracking, demodulation to navigation message parsing, and features configurable and scalable algorithm module interfaces to meet the needs of scientific research analysis and real-time applications. Second, it implements real-time GLONASS new system signal reception link processing, establishing a complete real-time signal processing link, including key stages such as front-end data acquisition, frequency and code synchronization, carrier tracking loop, secondary code synchronization, message deframes, and ephemeris extraction, through multi-channel threads. Parallel processing, single instruction multiple data instruction (SID), and efficient cache management ensure real-time signal processing performance even under multi-channel conditions. Thirdly, a real-time GLONASS new system signal quality analysis platform is constructed, designing and implementing signal quality monitoring and analysis functions. It outputs real-time indicators such as carrier lock status, signal-to-noise ratio, second-level code synchronization accuracy, frame synchronization success rate, and message decoding accuracy. It comprehensively evaluates the GLONASS new system signal quality across five domains: time domain, frequency domain, correlation domain, modulation domain, and measurement domain. Supporting visualization and log recording, this platform can be used to evaluate the modulation quality, reception robustness, and system integrity of signals at different frequencies, providing a basis for engineering verification and performance evaluation of the GLONASS new system signal.

[0136] The design goals for the GLONASS new system signal real-time software receiver and signal analysis platform are as follows: 1) Achieve real-time reception and demodulation of L1OC, L2OC, and L3OC signals across the entire link; 2) Achieve single-instruction multiple-data parallel real-time processing in a normal CPU environment with a processing latency of <1ms; 3) Supports real-time decoding and output of navigation messages and ephemeris data; 4) Provides signal quality analysis and system performance monitoring functions, which can be used for signal evaluation and experimental verification.

[0137] Through the above design, this invention proposes a signal analysis method for a real-time software receiver of the GLONASS new system signal, which can not only meet the needs of scientific research experiments, but also provide an algorithmic basis and reference implementation for subsequent hardware receiver design and multi-system compatibility research.

[0138] To comprehensively verify the actual performance of satellite transmitted signals, it is essential to conduct celestial observation and analysis in a real space environment. While simulated signals or experimental sources can verify the feasibility of algorithms, they cannot reflect real physical phenomena such as satellite power distribution, spectral characteristics, ionospheric propagation effects, multipath interference, and time synchronization deviations. Analyzing real space signals allows for the evaluation of system performance across multiple dimensions, including carrier and code stability, signal-to-noise ratio variation, modulation spectrum consistency, inter-symbol interference, and navigation message accuracy. These indicators not only relate to the reliability verification of the software receiver but also directly impact subsequent precise positioning and time synchronization performance. Therefore, designing a generalized signal quality analysis module in this invention is of great significance. This module can perform multi-level and multi-dimensional comprehensive analysis of received GLONASS new system signals, covering the following analysis scope: 1) Time domain (waveform distortion, eye diagram); 2) Frequency domain (power spectral density, composite power spectral deviation analysis); 3) Correlation domain (correlation loss analysis, S-curve zero-crossing deviation and slope); 4) Modulation domain (constellation diagram, carrier phase deviation, etc.); 5) Measurement domain (code-carrier phase consistency, inter-code phase consistency, etc.).

[0139] Through the above multi-dimensional joint evaluation, the platform can achieve systematic quality analysis of signals at various frequency points of GLONASS L1OC, L2OC, and L3OC, providing objective data support for signal performance verification, algorithm optimization, and multi-system compatibility research.

[0140] Compared to existing technologies, current platforms for full-link real-time software receiver architectures for the new GLONASS (CDMA) system generally remain at the level of the old GLONASS FDMA system or only cover acquisition / tracking, lacking the full-process capability of "real-time acquisition-tracking-demodulation-message parsing" for L1OC / L2OC / L3OC. Therefore, this invention proposes and implements a general SDR receiver framework that supports GLONASS signals, which can simultaneously meet the needs of scientific research analysis and engineering applications. Simultaneously, it implements an engineering acceleration scheme for multi-channel real-time processing on a regular CPU. One aspect is pre-computation for real-time processing: sine and cosine signals are pre-generated using a carrier lookup table (CarrierLUT), and all possible results of "input 8-bit IQ × ​​carrier" are pre-computed into mix_tbl. The system employs several key features. First, it replaces multiplication / trigonometric operations with table lookup, significantly reducing CPU load. Second, it utilizes SIMD vectorization, cache management, and multi-threaded parallelism: critical operations such as correlation are parallelized using SIMD (AVX2256-bit, processing multiple samples in parallel at once), combined with multi-channel thread parallelism and efficient cache management. Furthermore, it integrates a real-time signal quality analysis platform and a five-domain indicator system, achieving "processing + evaluation." Above receiver tracking, it directly extracts intermediate and measured quantities to form a real-time quality monitoring function, covering five major domains: time domain, frequency domain, correlation domain, modulation domain, and measurement domain. It emphasizes visualization and log recording, and can output core indicators such as spectrum status, modulation constellation diagram, lock status, C / N0, second-level code synchronization accuracy, frame synchronization success rate, and message decoding accuracy in real time.

[0141] In summary, this invention possesses full-link software receiver capabilities for the new GLONASS architecture, covering the complete signal and information processing flow from intermediate frequency sampling, acquisition / tracking to secondary code synchronization, frame synchronization, message decoding, ephemeris analysis, and measurement output. It can be directly used for signal engineering verification and performance evaluation of the new GLONASS architecture. Simultaneously, it boasts strong real-time performance (SIMD / AVX2 acceleration), improving throughput in core processes such as mixing and correlation through vectorized parallelism and operator-level optimization using SIMD / AVX2, achieving multi-channel, low-latency real-time processing capabilities on a general-purpose CPU. Furthermore... This solution achieves integrated spatial signal quality assessment across five domains. It integrates a quality analysis module within the receiver link, providing a unified assessment of signal quality from five dimensions: time, frequency, correlation, modulation, and measurement. It can output key indicators such as lock status, C / N0, and synchronization / decoding success rate in real time, supporting visualization and log recording. Compared to hardware receivers, this solution is lower in cost and facilitates digital analysis. Software implementation reduces R&D and deployment costs, facilitates rapid iteration and parameter configuration, and allows direct extraction of intermediate and measured quantities for standardized output, significantly improving the interpretability and reproducibility of signal quality analysis.

[0142] In this embodiment of the invention, a signal analysis method for a real-time software receiver of GLONASS new system signals is provided. The method acquires the GLONASS new system signal and the local PRN code, and preprocesses the GLONASS new system signal using a preset Doppler frequency table based on a predefined number of sampling points to generate multiple sets of intermediate frequency (IF) signals. Signal acquisition is performed based on the local PRN code, the multiple sets of IF signals, and a preset coherence integration time, outputting the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio (CNR). Signal tracking is then performed based on a pre-generated resampled code library, the captured Doppler precision estimate, the initial code phase, the CNR, and the multiple sets of IF signals. The system generates a baseband low-frequency signal and six sets of correlation results. Based on these six sets of correlation results, the baseband low-frequency signal, and preset ideal signal parameters, signal analysis is performed, outputting code phase observations and five-domain signal quality indicators. Using preset CRC check rules, signal synchronization decoding is performed based on the six sets of correlation results, a preset second-level code parameter table, a preset convolutional code rate, and a preset frame synchronization header, outputting a valid navigation message bitstream. Based on the valid navigation message bitstream, five-domain signal quality indicators, code phase observations, and an approximate initial receiver position, receiver positioning calculations are performed, outputting the receiver's final three-dimensional position. Based on the above scheme, this invention obtains new GLONASS data. After processing the system signal and local PRN code, the signal is preprocessed using a preset Doppler frequency table and a predefined number of sampling points to generate multiple sets of intermediate frequency (IF) signals, enabling early signal normalization and adaptation. Combining the local PRN code, multiple IF signals, and a preset coherent integration time, signal acquisition calculations are performed, outputting the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio. This efficiently extracts key signal acquisition parameters without requiring additional complex computational processes, significantly improving signal acquisition speed. Based on a pre-generated resampling code library, signal tracking calculations are performed using the captured output parameters and multiple IF signals to generate a baseband low-frequency signal and six sets of correlation structures. The system can fully reuse pre-configured resources, simplifying the signal matching and processing flow during tracking; based on six sets of related results, baseband low-frequency signals, and preset ideal signal parameters, it performs signal analysis and outputs code phase observations and five-domain signal quality indicators, focusing on core output requirements, avoiding redundant data calculation and output, reducing computational load while improving analysis efficiency; following preset CRC check rules, it performs signal synchronous decoding operations in combination with six sets of related results and various preset parameters, and outputs a valid navigation message bit stream, which can efficiently complete signal synchronous decoding according to established specifications, avoiding repeated trial and error in the parsing process, and quickly obtaining the navigation message data required for positioning;Finally, by integrating the effective navigation message bitstream, five-domain signal quality indicators, code phase observations, and approximate initial receiver position, positioning calculations are performed, and the final three-dimensional position of the receiver is output. This achieves efficient integration of navigation data and positioning computation, rapidly completing the positioning solution and outputting the result. The synergistic effect of the above steps ensures that the software receiver can quickly complete the entire process from signal acquisition to positioning result output, fully meeting the real-time requirements of practical applications.

[0143] Please see Figure 8 , Figure 8 This is a structural block diagram of a signal analysis system for a real-time software receiver of a new GLONASS signal system, provided in Embodiment 2 of the present invention.

[0144] This invention provides a signal analysis system for a real-time software receiver of a new GLONASS signal system, comprising: The signal preprocessing module 801 is used to acquire the GLONASS new system signal and the local PRN code, and to perform signal preprocessing on the GLONASS new system signal according to the preset Doppler frequency table and the number of predefined sampling points to generate multiple sets of intermediate frequency signals. The signal acquisition module 802 is used to acquire signals based on the local PRN code, multiple intermediate frequency signals, and a preset coherent integration time, and outputs the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio. The signal tracking module 803 is used to track signals based on a pre-generated resampled code library, captured Doppler precision estimates, initial code phase, carrier-to-noise ratio and multiple sets of intermediate frequency signals, and generate baseband low-frequency signals and six sets of correlation results. The signal analysis module 804 is used to perform signal analysis based on six sets of correlation results, baseband low-frequency signal and preset ideal signal parameters, and output code phase observation and five-domain signal quality index. The signal synchronization decoding module 805 is used to perform signal synchronization decoding based on six sets of related results, a preset second-level code parameter table, a preset convolutional code rate, and a preset frame synchronization header using preset CRC check rules, and output a valid navigation message bit stream. The positioning module 806 is used to perform receiver positioning calculations based on the effective navigation message bit stream, five-domain signal quality indicators, code phase observations, and approximate initial position of the receiver, and output the final three-dimensional position of the receiver.

[0145] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0146] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the signal analysis method of the GLONASS new system signal real-time software receiver as described in the above embodiments.

[0147] This invention also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the signal analysis method for the GLONASS new system signal real-time software receiver as described in the above embodiments.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed system methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0149] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A signal analysis method for a real-time software receiver of a new GLONASS signal system, characterized in that, include: The GLONASS new system signal and local PRN code are acquired, and the GLONASS new system signal is preprocessed using a preset Doppler frequency table according to a predefined number of sampling points to generate multiple sets of intermediate frequency signals. Signal acquisition is performed based on the local PRN code, multiple sets of intermediate frequency signals, and a preset coherent integration time, and the captured Doppler precision value, initial code phase, and carrier-to-noise ratio are output. Based on the pre-generated resampled code library, the captured Doppler precision estimate, the initial code phase, the carrier-to-noise ratio, and multiple sets of intermediate frequency signals, signal tracking is performed to generate a baseband low-frequency signal and six sets of correlation results; Based on the six sets of related results, the baseband low-frequency signal and the preset ideal signal parameters, signal analysis is performed to output code phase observations and five-domain signal quality indicators. The signal is synchronously decoded using a preset CRC check rule based on the six sets of related results, a preset second-level code parameter table, a preset convolutional code rate, and a preset frame synchronization header, and a valid navigation message bit stream is output. The receiver positioning is calculated based on the effective navigation message bit stream, the five-domain signal quality index, the code phase observation, and the approximate initial position of the receiver, and the final three-dimensional position of the receiver is output.

2. The signal analysis method for the real-time software receiver of the new GLONASS signal system according to claim 1, characterized in that, The GLONASS new system signal is preprocessed using a preset Doppler frequency table according to a predefined number of sampling points to generate multiple sets of intermediate frequency signals, including: The GLONASS new system signal is converted into 8-bit complex-sampled I / Q data; Calculate the sine and cosine sequences based on the predefined number of sampling points; Based on the sine sequence, the cosine sequence, and the 8-bit complex sampled I / Q data, multiple mixing results are pre-calculated; Based on the multiple mixing results, a carrier mixing table is generated; Based on the preset Doppler frequency table, candidate frequency offset intervals covering the effects of satellite motion and receiver clock bias are divided, and a Doppler candidate frequency offset set is constructed. The 8-bit complex sampled I / Q data is formatted, and the formatted 8-bit complex sampled I / Q data is output. Based on the Doppler candidate frequency offset set and the carrier mixing table, a lookup table mixing operation is performed point by point on the formatted 8-bit complex sampled I / Q data to obtain multiple sets of intermediate frequency signals.

3. The signal analysis method for the real-time software receiver of the new GLONASS signal system according to claim 1, characterized in that, The step of acquiring signals based on the local PRN code, multiple sets of intermediate frequency signals, and a preset coherent integration time, and outputting the captured Doppler precision estimate, initial code phase, and carrier-to-noise ratio, includes: Perform single-instruction multiple-data-stream parallel multiplication and fast Fourier transform on multiple sets of intermediate frequency signals and local PRN codes to obtain multiple related output matrices; Based on the preset coherent integration time, coherent integration is performed on each of the relevant output matrices to generate multiple I / Q data pairs. The amplitude squares of the multiple I / Q data pairs are calculated and accumulated to obtain a two-dimensional incoherent integrated power map. In the two-dimensional incoherent integral power graph, the maximum correlation peak and the average noise power are searched, and the carrier-to-noise ratio is calculated using the maximum correlation peak and the average noise power. Compare the carrier-to-noise ratio with the preset carrier-to-noise ratio threshold; If the carrier-to-noise ratio is greater than or equal to the preset carrier-to-noise ratio threshold, then extract the Doppler index corresponding to the maximum correlation peak and the Doppler frequencies and incoherent integral amplitudes of the left and right neighboring points of the Doppler index. Using the Doppler frequencies of the left and right neighboring points as independent variables and the corresponding incoherent integral amplitudes as dependent variables, the captured Doppler precision estimate is obtained by least squares polynomial fitting. The initial code phase is obtained by converting the code phase index corresponding to the maximum correlation peak value, the chip length of the local PRN code, and the sampling rate.

4. The signal analysis method for the real-time software receiver of the new GLONASS signal system according to claim 1, characterized in that, The signal tracking is performed based on the pre-generated resampled code library, the captured Doppler precision estimate, the initial code phase, the carrier-to-noise ratio, and multiple sets of intermediate frequency signals to generate a baseband low-frequency signal and six sets of correlation results, including: Based on the captured Doppler precision estimate, the initial code phase, and the carrier-to-noise ratio, the initial state of the tracking loop is determined; Based on the initial state of the tracking loop and the target intermediate frequency signal corresponding to successful acquisition from multiple sets of intermediate frequency signals, a local replica carrier is generated; The target intermediate frequency signal is mixed with the local replicated carrier to obtain the baseband low frequency signal; The lead pseudocode, instantaneous pseudocode, and lag pseudocode in the pre-generated resampling code library are subjected to time-domain correlation operations with the baseband low-frequency signal to obtain six sets of correlation results.

5. The signal analysis method for a real-time software receiver of the new GLONASS signal system according to claim 1, characterized in that, The signal analysis, based on the six sets of correlation results, the baseband low-frequency signal, and preset ideal signal parameters, outputs code phase observations and five-domain signal quality indicators, including: The real-time carrier-to-noise ratio is calculated based on the amplitude of the six sets of related results; The real-time carrier-to-noise ratio is compared with a preset lock-and-hold threshold. If the real-time carrier-to-noise ratio is greater than the preset lock-and-hold threshold, then a carrier phase observation and a code phase observation are generated. Based on the carrier phase observation, the code phase observation, and the preset ideal signal parameters, time-domain analysis is performed on the baseband low-frequency signal; frequency-domain analysis is performed on the signal spectrum obtained by Fourier transform of the baseband low-frequency signal; correlation-domain analysis is performed on the code correlation characteristics corresponding to the six sets of correlation results; modulation-domain analysis is performed on the I / Q modulation components obtained by splitting the baseband low-frequency signal; and measurement-domain analysis is performed on the phase relationship between the carrier phase observation and the code phase observation, outputting a five-domain signal quality index.

6. The signal analysis method for a real-time software receiver of the new GLONASS signal system according to claim 1, characterized in that, The method employs a preset CRC checksum rule to perform signal synchronization decoding based on the six sets of related results, a preset second-level code parameter table, a preset convolutional code rate, and a preset frame synchronization header, outputting a valid navigation message bit stream, including: Based on the six sets of related results and the preset secondary code parameter table, a local secondary code is generated; A matching operation is performed between the six sets of related results and the local secondary code to obtain the synchronized secondary code; Multiply the six sets of related results with the synchronized binary code to obtain a pure symbol stream; Based on the preset convolutional code rate, the Viterbi algorithm convolutional decoding is performed on the pure symbol stream to restore it to the information bit stream; Based on the preset frame synchronization header and the preset CRC check rule, the frame start position is searched in the information bit stream; Perform CRC check on the entire frame data corresponding to the frame start position and output a valid navigation message bit stream.

7. The signal analysis method for a real-time software receiver of the new GLONASS signal system according to claim 1, characterized in that, The step of calculating the receiver positioning based on the effective navigation message bit stream, the five-domain signal quality index, the code phase observation, and the approximate initial position of the receiver, and outputting the final three-dimensional position of the receiver, includes: Identify the string type in the valid navigation message bit stream, and based on the string type, call the bit parsing function to extract the satellite data field; The quantized values ​​corresponding to the satellite data fields are converted into physical quantities, and a standardized ephemeris and time parameter set is output. The orbit extrapolation process is performed on the ephemeris data in the standardized ephemeris and time parameter set to obtain the three-dimensional coordinates of the satellite target at that time. Perform coordinate system one and geometric operations on the three-dimensional coordinates of the satellite target at the time and the approximate value of the receiver's initial position to obtain the geometric distance between the satellite and the receiver, the satellite's azimuth angle and elevation angle; The code phase observation is weighted and corrected by the five-domain signal quality index, the satellite azimuth angle and the elevation angle to obtain the weighted and corrected code phase observation. Based on the three-dimensional coordinates of the satellite target at the specified time, the geometric distance between the satellite and the receiver, and the weighted and corrected code phase observations, multiple sets of pseudorange equations are constructed. Based on the approximate initial position of the receiver, a first-order Taylor expansion linearization process is performed on multiple sets of pseudorange equations to obtain a linear observation equation set. The least squares algorithm is applied to the linear observation equations to obtain the receiver position correction and clock error correction. Based on the receiver position correction amount and the clock error correction amount, the approximate value of the initial position of the receiver is updated to obtain the updated three-dimensional position of the receiver. A threshold comparison is performed between the receiver position correction amount and the clock error correction amount. If both the receiver position correction amount and the clock error correction amount are less than the corresponding preset threshold, then the updated receiver three-dimensional position is taken as the final three-dimensional position of the receiver.

8. A signal analysis system for a real-time software receiver of a new GLONASS signal system, characterized in that, include: The signal preprocessing module is used to acquire the GLONASS new system signal and the local PRN code, and to perform signal preprocessing on the GLONASS new system signal according to a preset Doppler frequency table and a predefined number of sampling points to generate multiple sets of intermediate frequency signals. The signal acquisition module is used to acquire signals based on the local PRN code, multiple sets of intermediate frequency signals, and a preset coherent integration time, and output the captured Doppler precision value, initial code phase, and carrier-to-noise ratio. The signal tracking module is used to track signals based on a pre-generated resampled code library, the captured Doppler precision value, the initial code phase, the carrier-to-noise ratio, and multiple sets of intermediate frequency signals, and generate a baseband low-frequency signal and six sets of correlation results; The signal analysis module is used to perform signal analysis based on the six sets of correlation results, the baseband low-frequency signal, and preset ideal signal parameters, and output code phase observations and five-domain signal quality indicators. The signal synchronization decoding module is used to perform signal synchronization decoding based on the six sets of related results, the preset second-level code parameter table, the preset convolutional code rate, and the preset frame synchronization header using preset CRC check rules, and output a valid navigation message bit stream. The positioning module is used to perform receiver positioning calculations based on the effective navigation message bit stream, the five-domain signal quality index, the code phase observation, and the receiver's initial position approximation, and outputs the receiver's final three-dimensional position.

9. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the signal analysis method for a real-time software receiver of a new GLONASS signal system as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the signal analysis method of the GLONASS new system signal real-time software receiver as described in any one of claims 1-7.

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