Self-adaptive dual-frequency joint acquisition method, system and device based on signal-to-noise ratio estimation and medium

Abstract

The invention discloses a signal-to-noise ratio estimation-based self-adaptive dual-frequency joint acquisition method, system and device and a medium, and relates to the technical field of satellite navigation communication, and the method comprises the following steps: obtaining a digital intermediate frequency signal; performing parallel correlation processing on a first frequency point signal and a second frequency point signal in the digital intermediate frequency signal to generate a first correlation peak matrix and a second correlation peak matrix; estimating a signal-to-noise ratio based on the first correlation peak matrix; generating a first adaptive weight coefficient based on the signal-to-noise ratio, and determining a second adaptive weight coefficient based on the first adaptive weight coefficient; respectively applying the first adaptive weight coefficient and the second adaptive weight coefficient to carry out weighted fusion to generate a fusion judgment matrix; and searching a maximum peak value in the fusion judgment matrix, and when the maximum peak value exceeds a preset fusion judgment threshold, determining that the capture is successful. Decision oscillation can occur at the critical point of the signal-to-noise ratio, and decision jitter is avoided by distributing weights and utilizing information of two channels to carry out fusion judgment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation and communication technology, and particularly to an adaptive dual-frequency joint acquisition method and system based on signal-to-noise ratio estimation, a device and a medium. BACKGROUND

[0002] Global Navigation Satellite System (GNSS) plays an increasingly important role in critical infrastructure such as power systems, transportation, and communication. As a core component of GNSS, the BeiDou Satellite Navigation System (BDS) is accelerating its global application. In order to improve the accuracy, availability and reliability of positioning, modern GNSS receivers (such as the "single-BeiDou multi-frequency module" described in the background) are designed to be able to process signals at multiple frequencies simultaneously, such as the B1I and B1C frequencies of the BeiDou system.

[0003] Signal acquisition is the first step of GNSS receiver operation, and its purpose is to determine whether there is a visible satellite signal in the two-dimensional search space composed of (code phase, Doppler frequency shift), and to roughly estimate its parameters.

[0004] In the field of GNSS signal acquisition, there is an inherent technical contradiction between acquisition sensitivity and acquisition speed.

[0005] On the one hand, in order to capture weak satellite signals (such as in indoor, urban canyons or strong interference environments), it is necessary to improve the acquisition sensitivity. The main technical means to improve sensitivity is to extend the coherent integration time. For example, extending the coherent integration time from the traditional 1 millisecond (ms) to 10 milliseconds (ms) or longer can significantly improve the signal-to-noise ratio (SNR) of the correlation peak.

[0006] On the other hand, extending the coherent integration time will have two adverse effects: first, it significantly increases the computational burden of baseband processing (for example, the number of points of Fast Fourier Transform (FFT) increases with the integration time), thus prolonging the total time required to traverse the entire search space and reducing the acquisition speed; second, it places higher requirements on the frequency stability of the receiver's local crystal oscillator and increases the risk of correlation loss due to data bit transitions.

[0007] In order to balance this contradiction, especially for BeiDou B1I and B1C dual-frequency signals, existing technologies have proposed some joint acquisition schemes.

[0008] The prior art proposes an adaptive Beidou dual-frequency joint acquisition method based on SNR estimation. The idea of this method is: first, estimate the SNR of the current environment; then, according to the estimated SNR value, make a binary switch or a hard decision: if the SNR estimation value is high (strong signal), the system selects to process B1I signal (1ms integration) to pursue speed; if the SNR estimation value is low (weak signal), the system selects to process B1C signal (10ms integration) to pursue sensitivity.

[0009] This scheme introduces a new technical problem: the performance of this method depends heavily on the accuracy of SNR estimation. Near the critical point of SNR, a small fluctuation of SNR estimation value (for example, jumping between 30dB-Hz and 31dB-Hz) will cause the system decision to oscillate violently between "processing only B1I" and "processing only B1C". This oscillation will lead to: 1) if B1I is selected in a weak signal, it will lead to acquisition failure; 2) if B1C is selected in a strong signal, it will lead to unnecessary prolongation of acquisition time. Related research has pointed out that replacing "hard decision" with "soft decision" is the key to improving system robustness. However, the solution is to solve the decision fusion of anti-deception, not the fusion of signal acquisition.

[0010] In summary, the prior art has failed to provide an effective technical solution to the technical problem of how to robustly and efficiently utilize the speed advantage of B1I signal (1ms) and the sensitivity advantage of B1C signal (10ms) at the same time in a wide dynamic SNR range. SUMMARY

[0011] In view of the above problems, the present application provides an adaptive dual-frequency joint acquisition method, system, device and medium based on SNR estimation.

[0012] Therefore, the problem to be solved by the present application is that the performance of the prior art method depends heavily on the accuracy of SNR estimation. Near the critical point of SNR, a small fluctuation of SNR estimation value will cause the system decision to oscillate violently between "processing only B1I" and "processing only B1C".

[0013] To solve the above technical problems, the application provides the following technical scheme: an adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation, comprising: acquiring a digital intermediate frequency signal containing a first frequency point signal and a second frequency point signal; using a first coherent integration time length to perform parallel correlation processing on the first frequency point signal in the digital intermediate frequency signal to generate a first correlation peak matrix; using a second coherent integration time length to perform parallel correlation processing on the second frequency point signal in the digital intermediate frequency signal to generate a second correlation peak matrix; the second coherent integration time length is greater than the first coherent integration time length; estimating the signal-to-noise ratio of the digital intermediate frequency signal based on the first correlation peak matrix; inputting the signal-to-noise ratio into a nonlinear mapping function to generate a first adaptive weight coefficient, determining a second adaptive weight coefficient based on the first adaptive weight coefficient; applying the first adaptive weight coefficient and the second adaptive weight coefficient to the corresponding search units in the first correlation peak matrix and the second correlation peak matrix, respectively, for weighted fusion to generate a fusion decision matrix; searching for a maximum peak value in the fusion decision matrix, and when the maximum peak value exceeds a preset fusion decision threshold, determining that acquisition is successful, and outputting the code phase and Doppler frequency shift corresponding to the maximum peak value.

[0014] As a preferred scheme of the adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation, the acquisition of the digital intermediate frequency signal containing the first frequency point signal and the second frequency point signal comprises: receiving a radio frequency signal from a satellite by a radio frequency front end, and performing low-noise amplification processing on the radio frequency signal; performing frequency domain selection and frequency mapping processing on the radio frequency signal amplified by the low noise, converting the radio frequency signal to the intermediate frequency channels of the corresponding first frequency point and second frequency point respectively, to obtain an analog intermediate frequency signal containing target frequency point energy; performing analog-to-digital conversion on the intermediate frequency channels of the first frequency point and the second frequency point respectively to generate corresponding digital intermediate frequency sampling sequences, and inputting the digital intermediate frequency sampling sequences into a baseband processor to form a digital intermediate frequency signal containing the first frequency point signal and the second frequency point signal.

[0015] The preferred technical scheme has the beneficial effect that: through selective filtering and frequency mapping of the target frequency band, out-of-band interference and image interference can be effectively suppressed, the data quality entering the baseband is higher, and the stability of subsequent correlation processing is beneficial.

[0016] As a preferred scheme of the adaptive dual-frequency joint acquisition method based on SNR estimation, the generating of the first correlation peak matrix comprises: time slicing the first frequency point signal according to a first coherent integration time length, sending the digital intermediate frequency data corresponding to each time length into a local pseudo code generator and a local carrier generation module, and generating a plurality of groups of local reference signals under different code phases and Doppler frequency shifts; calculating the matching degree of the digital intermediate frequency signal and the local reference signal, generating the correlation output results covering the first frequency point search space within the first coherent integration time length by using a correlation operation structure with parallel processing capability; organizing the correlation output results into a two-dimensional matrix form according to the code phase index and the Doppler index, so that each matrix element corresponds to the correlation energy value under a specific code phase and a specific Doppler frequency shift, and a first correlation peak matrix for first frequency point signal acquisition decision is formed.

[0017] As a preferred scheme of the adaptive dual-frequency joint acquisition method based on SNR estimation, the generating of the first correlation peak matrix comprises: time slicing the first frequency point signal according to a first coherent integration time length, sending the digital intermediate frequency data corresponding to each time length into a local pseudo code generator and a local carrier generation module, and generating a plurality of groups of local reference signals under different code phases and Doppler frequency shifts; calculating the matching degree of the digital intermediate frequency signal and the local reference signal, generating the correlation output results covering the first frequency point search space within the first coherent integration time length by using a correlation operation structure with parallel processing capability; organizing the correlation output results into a two-dimensional matrix form according to the code phase index and the Doppler index, so that each matrix element corresponds to the correlation energy value under a specific code phase and a specific Doppler frequency shift, and a first correlation peak matrix for first frequency point signal acquisition decision is formed.

[0018] The beneficial effects of the preferred technical scheme are that the noise statistical method after excluding the peak value can avoid the deviation of the noise reference caused by local abnormal points or isolated interference, and the peak-to-noise ratio generated by the statistical ratio of the maximum peak value and the noise base makes the estimation result highly related to the actual signal strength.

[0019] As a preferred scheme of the adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation, after the signal-to-noise ratio is obtained, the normalization of the first correlation peak matrix and the second correlation peak matrix comprises that a noise base value is taken as a normalization reference, and the value of each unit in the first correlation peak matrix is quantized by the same noise reference; the correlation output of all matrix units in the first correlation peak matrix is adjusted in amplitude one by one by taking the noise base value as a scale factor, so that the value of each matrix unit reflects the energy ratio relative to the noise background, thereby forming a first normalized correlation peak matrix; the corresponding noise base value acquisition and amplitude normalization processing are performed on the second correlation peak matrix in the same way as the first correlation peak matrix, to obtain a second normalized correlation peak matrix.

[0020] As a preferred scheme of the adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation, the generation of the first adaptive weight coefficient comprises that a nonlinear mapping model for describing the change relationship of the weight with the signal-to-noise ratio is constructed according to the preset signal-to-noise ratio switching center point parameter and the slope gain parameter; the signal-to-noise ratio estimation value obtained based on the first correlation peak matrix is taken as an input into the nonlinear mapping model; after the nonlinear mapping is completed, the value of the mapping output is extracted as the first adaptive weight coefficient; the second adaptive weight coefficient is obtained by subtracting the first adaptive weight coefficient from 1.

[0021] As a preferred scheme of the adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation, the output of the code phase and the Doppler frequency shift corresponding to the maximum peak value comprises that the fusion decision matrix obtained after weighting fusion is scanned, the global maximum value of the correlation output is identified as the maximum peak value in the matrix area covering all code phase and Doppler frequency shift combinations, and the index position corresponding to the maximum peak value is obtained; the maximum peak value is compared with a preset fusion decision threshold, when the maximum peak value exceeds the threshold in energy, it is determined that there is a peak response representing the presence of an effective satellite signal, and it is determined that the current digital intermediate frequency signal contains a satellite signal that can be acquired, thereby completing the confirmation of the acquisition state; after the acquisition is confirmed to be successful, the code phase index value and the Doppler frequency shift index value are extracted from the matrix index corresponding to the maximum peak value, the two indexes are converted into actual coarse code phase estimation and coarse Doppler frequency shift estimation, and the estimation results are taken as initial conditions for entering a tracking loop.

[0022] To solve the above technical problems, the application provides the following technical scheme: an adaptive dual-frequency joint acquisition system based on SNR estimation, comprising an acquisition module, a correlation peak matrix calculation module, an SNR calculation module, a weighting module and an acquisition module; the acquisition module acquires a digital intermediate frequency signal containing a first frequency signal and a second frequency signal; the correlation peak matrix calculation module performs parallel correlation processing on the first frequency signal in the digital intermediate frequency signal by using a first coherent integration time length to generate a first correlation peak matrix, and performs parallel correlation processing on the second frequency signal in the digital intermediate frequency signal by using a second coherent integration time length to generate a second correlation peak matrix, wherein the second coherent integration time length is greater than the first coherent integration time length; the SNR calculation module estimates the SNR of the digital intermediate frequency signal based on the first correlation peak matrix; the weighting module inputs the SNR into a nonlinear mapping function to generate a first adaptive weight coefficient, determines a second adaptive weight coefficient based on the first adaptive weight coefficient, and applies the first adaptive weight coefficient and the second adaptive weight coefficient to the corresponding search units in the first correlation peak matrix and the second correlation peak matrix, respectively, to perform weighted fusion to generate a fusion decision matrix; and the acquisition module searches for a maximum peak value in the fusion decision matrix, and determines that acquisition is successful when the maximum peak value exceeds a preset fusion decision threshold, and outputs the code phase and the Doppler frequency shift corresponding to the maximum peak value.

[0023] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the adaptive dual-frequency joint acquisition method based on SNR estimation when executing the computer program.

[0024] A computer readable storage medium stores a computer program, and the computer program implements the steps of the adaptive dual-frequency joint acquisition method based on SNR estimation when executed by a processor.

[0025] The application has the following beneficial effects: the application uses "soft fusion" to completely replace the "hard switching" logic in the prior art. At the SNR critical point, the scheme will oscillate in decision-making, but the application will smoothly distribute the weight and make fusion decision-making by using the information of two channels, thereby avoiding decision-making jitter and improving the robustness of the system. The calculation overhead of SNR estimation, weight calculation and fusion is extremely low. Compared with the serial method of the prior art, the application avoids the fixed period penalty caused by the failure of B1I acquisition under a weak signal, and significantly improves the acquisition efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0027] Figure 1 A flow chart of an adaptive dual-frequency joint acquisition method based on SNR estimation in Embodiment 1.

[0028] Figure 2 An implementation flow chart of an adaptive dual-frequency joint acquisition method based on SNR estimation in Embodiment 2. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, features and advantages of the present application more apparent and comprehensible, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0030] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the concept of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0031] Embodiment 1, refer to Figure 1 , the first embodiment of the present application, the embodiment provides an adaptive dual-frequency joint acquisition method based on SNR estimation, which comprises: S1: obtaining a digital intermediate frequency signal containing a first frequency signal and a second frequency signal.

[0032] S2: using a first coherent integration time, performing parallel correlation processing on the first frequency signal in the digital intermediate frequency signal to generate a first correlation peak matrix.

[0033] S3: using a second coherent integration time, performing parallel correlation processing on the second frequency signal in the digital intermediate frequency signal to generate a second correlation peak matrix.

[0034] S4: the second coherent integration time is greater than the first coherent integration time, and the SNR of the digital intermediate frequency signal is estimated based on the first correlation peak matrix.

[0035] S5: inputting the SNR into a nonlinear mapping function to generate a first adaptive weight coefficient, and determining a second adaptive weight coefficient based on the first adaptive weight coefficient.

[0036] S6: Corresponding search units in the first correlation peak matrix and the second correlation peak matrix are respectively weighted and fused by applying the first adaptive weight coefficient and the second adaptive weight coefficient to generate a fusion decision matrix.

[0037] S7: A maximum peak in the fusion decision matrix is searched, and when the maximum peak exceeds a preset fusion decision threshold, it is determined that the acquisition is successful, and a code phase and a Doppler shift corresponding to the maximum peak are output.

[0038] It should be noted that the prior art relies on a fixed signal-to-noise ratio threshold to make a hard switching decision of "only selecting the first frequency point" or "only selecting the second frequency point". When the actual signal-to-noise ratio fluctuates slightly around the threshold, the system will frequently switch between the two frequency points, resulting in repeated jumping between success and failure of the acquisition result, and the acquisition stability is extremely poor. When the signal-to-noise ratio is slightly lower than the threshold and is misjudged as a high signal-to-noise ratio environment, the system will incorrectly select the first frequency point with a short integration time as the acquisition channel, and the first frequency point cannot form a recognizable correlation peak in a weak signal, resulting in a direct failure of the acquisition and the inability to enter the tracking loop.

[0039] Therefore, in order to solve the above problems, as shown in Figure 1 by S1-S7 steps, the double-frequency digital intermediate frequency signal is processed in parallel, the correlation search results under short integration and long integration conditions are constructed respectively, the signal-to-noise ratio of the current signal environment is derived from the fast correlation output of the first frequency point, and then the adaptive weight that can change continuously with the environment is generated by using the signal-to-noise ratio through a nonlinear model. Then, the normalized correlation matrices obtained by the two integration methods are fused according to the weight, so that the contribution of the frequency points under different intensity conditions is dynamically adjusted. After fusion, whether the satellite signal exists is determined by searching for the peak value of the fusion decision matrix and making a threshold decision, and the corresponding coarse code phase and Doppler information are output according to the position of the peak value, so as to realize adaptive double-frequency joint acquisition with high sensitivity and high efficiency.

[0040] Embodiment 2, referring to Figure 2 The second embodiment of the present application is different from the first embodiment in that: a kind of adaptive double-frequency joint acquisition method based on signal-to-noise ratio estimation further includes the following steps A1-A3 in step S1: obtaining digital intermediate frequency signal containing first frequency point signal and second frequency point signal. A1: The radio frequency front end receives radio frequency signals from the satellite, and performs low noise amplification processing on the radio frequency signals.

[0041] A2: Frequency domain selection and frequency mapping processing are performed on the low-noise-amplified radio frequency signals, and the radio frequency signals are converted to the intermediate frequency channels corresponding to the first frequency point and the second frequency point respectively to obtain analog intermediate frequency signals containing target frequency point energy.

[0042] A3: performing analog-to-digital conversion on the intermediate frequency channels of the first frequency point and the second frequency point respectively to generate corresponding digital intermediate frequency sampling sequences, and inputting the digital intermediate frequency sampling sequences into a baseband processor to form a digital intermediate frequency signal containing the first frequency point signal and the second frequency point signal.

[0043] Specifically, the hardware and software function division of the present application is as follows: the radio frequency front end (RF) receives the BDS satellite signal (B1I: 1561.098 MHz, B1C: 1575.42 MHz, B2a: 1176.45 MHz, etc.). After low noise amplification (LNA), filtering (SAW filter), down-conversion (Mixer) and analog-to-digital conversion (ADC), a plurality of digital intermediate frequency (IF) signals are generated. The present application mainly processes two digital IF signals corresponding to B1I (first channel) and B1C (second channel).

[0044] In the embodiment of the present application, in step A2, the frequency mapping processing adopts an analog filtering + mixing + down-conversion mode, including the following steps A211-A213: A211: performing band-pass filtering processing on the radio frequency signal received via the radio frequency front end and completed low noise amplification, suppressing the out-of-band interference by setting a filter matched with the target frequency band, so that the remaining signal only contains the frequency band energy corresponding to the first frequency point and the second frequency point.

[0045] A212: inputting the band-pass filtered signal into a mixing link, performing down-conversion operation with the local oscillator signal to convert the radio frequency signal to the first intermediate frequency channel and the second intermediate frequency channel respectively, and further eliminating the image interference and spurious frequency components generated by mixing by using the corresponding intermediate frequency filter to obtain two independent and effective analog intermediate frequency outputs.

[0046] A213: inputting the first frequency point intermediate frequency signal and the second frequency point intermediate frequency signal output by the above two analog intermediate frequency channels into the subsequent analog-to-digital conversion module, so that they are digitized and used as the input of the baseband processor, thereby forming a digital intermediate frequency signal containing the first frequency point signal and the second frequency point signal.

[0047] In an optional embodiment, the frequency mapping processing can also adopt a digital down-conversion mode, including the following steps A221-A223: A221: performing band-pass filtering on the low noise amplified radio frequency signal to obtain a wideband analog intermediate frequency signal covering the bandwidth required by the first frequency point and the second frequency point, and suppressing the out-of-band interference and irrelevant frequency components by adjusting the filter bandwidth.

[0048] A222: Directly performing analog-to-digital conversion on a wideband analog intermediate frequency signal, and generating corresponding local oscillator sequences for the first frequency point and the second frequency point in the digital domain respectively, converting the original wideband sampling signal into digital intermediate frequency channels corresponding to the first frequency point and the second frequency point through digital mixing, digital low-pass filtering and decimation operation.

[0049] A223: Taking the two digital intermediate frequency channels generated by the digital down-conversion link as inputs of the baseband processor, so that the sampling data in the two channels respectively carry effective energy of the first frequency point signal and the second frequency point signal, thereby forming a digital intermediate frequency signal meeting the subsequent acquisition processing requirements.

[0050] In another optional implementation, the frequency mapping processing can also adopt a zero intermediate frequency mode, including the following steps A231-A233: A231: Directly down-converting the radio frequency signal amplified by the low noise amplifier using a zero intermediate frequency local oscillator, so that the radio frequency signal is converted into two complex baseband signals in-phase and quadrature in one mixing, and the energy of the first frequency point and the second frequency point corresponding frequency bands is distributed in the baseband domain.

[0051] A232: Performing baseband filtering on the complex baseband signal, by selectively retaining two bandwidth ranges containing the energy of the first frequency point and the second frequency point, so that the signal exists in two independent band-limited channels in the complex baseband domain, while suppressing noise and interference components irrelevant to the target frequency point.

[0052] A233: Performing analog-to-digital conversion on the two segments of complex baseband signals after baseband band-limiting processing, and inputting them as digital intermediate frequency inputs into the baseband processor, so that the two-way digitized data respectively carry the first frequency point signal and the second frequency point signal, thereby realizing acquisition of the digital intermediate frequency signal.

[0053] It should be noted that separating the two frequency point signals in the analog front end or the digital front end ensures that the subsequent parallel correlation processing can be independently performed on signals with different integration time lengths, improving the acquisition sensitivity and search efficiency; through low noise amplification, filtering and down-conversion, the integrity of the first frequency point and the second frequency point signals can be maintained in high noise or low power scenarios, enhancing the weak signal acquisition performance.

[0054] Further, in step S2, generating the first correlation peak matrix includes the following steps B1-B3: B1: Time slice division is performed on the first frequency point signal according to a first coherent integration time length, and the digital intermediate frequency data corresponding to each time length is input into a local pseudo code generator and a local carrier generation module, and a plurality of groups of local reference signals are generated under different code phases and Doppler frequency shifts.

[0055] B2: Match the digital intermediate frequency signal with the local reference signal, and generate a correlation output result covering the first frequency point search space within the first coherent integration time by using a correlation operation structure with parallel processing capability.

[0056] B3: Organize the correlation output result into a two-dimensional matrix form according to the code phase index and the Doppler index, so that each matrix element corresponds to the correlation energy value under a specific code phase and a specific Doppler shift, and form a first correlation peak matrix for the first frequency point signal acquisition decision.

[0057] In the embodiment of the present application, in step B2, the correlation operation structure with parallel processing capability adopts PMF-FFT (Partial Matched Filter-Fast Fourier Transform) parallel correlation processing, including the following steps B211-B213: B211: Preprocess the digital intermediate frequency data through the partial matched filter structure, so that the input signal is preliminarily matched with the local pseudo code sequence and the local carrier sequence in a segmented form, thereby reducing the calculation amount of subsequent large-scale correlation operation and speeding up the search process.

[0058] B212: Send the signal block after partial matched filtering into the fast Fourier transform operation module, and perform batch correlation operation in the frequency domain, so that the matching degree results of different code phase and Doppler shift combinations can be quickly generated in the form of frequency domain convolution, thereby realizing large-scale parallel traversal of the search space.

[0059] B213: Perform energy accumulation on the above frequency domain correlation output within the first coherent integration time to obtain the correlation result corresponding to each group of code phase and Doppler shift combination, and use it to construct a parallel correlation output set covering the first frequency point acquisition search range.

[0060] Specifically, in the present embodiment, the first coherent integration time is configured as 1ms, and the PMF-FFT algorithm is used for fast parallel acquisition on a 1ms data block.

[0061] Output the first correlation peak matrix , which is a two-dimensional matrix with dimensions of code phase search step and Doppler shift search step .

[0062] In an optional embodiment, the correlation operation structure with parallel processing capability can also adopt FFT parallel acquisition implemented by frequency domain cyclic convolution for parallel correlation processing, including the following steps B221-B223: B221: Sample the digital intermediate frequency signal in whole and send it into the fast Fourier transform module, convert the original time domain signal into frequency domain form, and use it as a unified input representation for correlation operation.

[0063] B222: After the local pseudo-code sequence is Fourier transformed, point-by-point multiplication operation is performed between the frequency domain representation of the digital intermediate frequency signal, so that the matching degree of different code phases can be uniformly calculated in the frequency domain through the cyclic convolution mechanism and batch generation of correlation results.

[0064] B223: The inverse fast Fourier transform is performed on the result after the frequency domain multiplication, and energy accumulation is performed within the first coherent integration time, so that the entire code phase search process is completed in the form of fast correlation through cyclic convolution, and the correlation matching results covering all code phases and Doppler combinations are output.

[0065] In another optional embodiment, the correlation operation structure with parallel processing capability can also use the parallel capture mode of the time domain sliding correlator array, including the following steps B231-B233: B231: The digital intermediate frequency signal is continuously sent into a group of parallel sliding correlators, each correlator corresponding to a group of different local pseudo-code offsets and local carrier parameters, so that each correlator can perform parallel matching on multiple code phases and Doppler shifts in the time domain.

[0066] B232: In each sliding correlator, an accumulator is used to perform point-by-point multiplication and accumulation processing on the input data and the local reference sequence, to form a correlation degree measure for a specific code phase and Doppler parameter within the first coherent integration time, and to continuously update the correlation output of each channel.

[0067] B233: The correlation degree results output by all sliding correlators are collected according to their corresponding code phase indexes and Doppler indexes, so that the entire sliding correlator array can cover the complete search space at the same time, thereby obtaining a parallel correlation result set for constructing the first correlation peak matrix.

[0068] It should be noted that by constructing a multi-code phase, multi-Doppler parallel correlation framework, a large-scale search can be completed within the first coherent integration time, greatly shortening the capture time; the correlation output is organized into a two-dimensional matrix, making the subsequent decision and peak extraction more efficient, and facilitating fusion after alignment with the second frequency point.

[0069] Further, in step S3, the digital IF signal of the B1C frequency point (the B1C signal has data / pilot duplex components, and the application preferably processes the pilot component because it has no data bit jump).

[0070] The second coherent integration time is configured, which is 10 ms in this embodiment. Since the integration time is 10 times of 1 ms, its sensitivity is theoretically about 10 dB higher than that of the B1I channel.

[0071] The PMF-FFT algorithm can be used to perform parallel acquisition on a 10 ms data block, and output a second correlation peak matrix .

[0072] The generated and two matrices are sent to the shared memory (DDR) through the bus.

[0073] Further, in step S4, estimating the signal-to-noise ratio of the digital intermediate frequency signal based on the first correlation peak matrix includes the following steps D1-D3: D1: Extracting the correlation output covering all code phase and Doppler combinations in the first correlation peak matrix, obtaining the maximum correlation peak value corresponding to the first frequency point signal by searching for the global maximum value in the matrix, and taking the maximum correlation peak value as a representation of the effective signal energy in the current environment.

[0074] D2: Statistically analyzing other matrix elements in the first correlation peak matrix that do not contain the maximum correlation peak value, extracting a representative value reflecting the background noise level from the correlation output of other matrix elements in the region, and taking the representative value as the noise floor value of the current search space.

[0075] D3: Generating a corresponding peak-to-noise ratio metric based on the ratio between the maximum correlation peak value and the noise floor value, and converting the peak-to-noise ratio metric into an estimated result in units of signal-to-noise ratio through a predetermined mapping relationship.

[0076] In the embodiments of the present application, in step D2, the noise floor value is estimated using the average value, including the following steps D211-D213: D211: Excluding the maximum peak value element representing the effective signal energy in the first correlation peak matrix, and regarding the remaining all matrix elements as sampling points of background noise to ensure that the noise statistics are not disturbed by the target peak value.

[0077] D212: Iteratively counting the background noise sampling points in the entire matrix region, calculating the average value of the correlation output of these elements, so that the average value can reflect the noise level of the current search space in a holistic sense.

[0078] D213: Taking the obtained average value as the noise floor value, and using it in the subsequent peak-to-noise ratio generation process, so that the subsequent signal-to-noise ratio estimation can be calculated based on a unified noise reference.

[0079] Specifically, side analysis the matrix, calculate its maximum peak value and noise floor value . The noise floor value can be obtained by averaging all non-peak elements in the matrix.

[0080] signal-to-noise ratio (peak-to-average ratio) .

[0081] Convert the noise floor value to dB-Hz by a pre-calibrated lookup table or empirical formula . For example, , where is a calibration constant related to 1ms integration.

[0082] In an alternative embodiment, the noise floor value can also be estimated by quantile noise estimation, including the following steps D221-D223: D221: Collect the correlation outputs of the remaining matrix units after excluding the maximum peak unit from the first correlation peak matrix, and consider these outputs as a sample set of background noise distribution for statistical analysis of quantile characteristics.

[0083] D222: Sort the noise sample set by amplitude, and select the value at the pre-set quantile point as the noise representative, such as selecting the quantile point close to the lower distribution section to enhance the shielding ability of interference peaks and abnormal protrusions.

[0084] D223: Take the value corresponding to the quantile point as the noise floor value, which can still provide a more robust noise level representation in the case of uneven background noise distribution or local interference, supporting subsequent peak-to-noise ratio estimation.

[0085] In another alternative embodiment, the noise floor value can also be estimated by median noise estimation, including the following steps D231-D233: D231: Collect all matrix units in the first correlation peak matrix except the maximum peak, and take these as sample values of background noise to ensure that the noise estimation process is not disturbed by the target peak.

[0086] D232: Sort the noise samples by their correlation output values and find the middle position in the sorted order, so that the value corresponding to this position can represent the typical level of noise distribution, thereby avoiding the influence of individual high noise points or interference points.

[0087] D233: Take the value corresponding to the middle position as the noise floor energy, so that the noise estimation remains highly robust in the presence of burst interference or uneven matrix distribution, improving the stability of subsequent peak-to-noise ratio and signal-to-noise ratio calculations.

[0088] It should be noted that the peak-to-noise ratio is generated by the statistical ratio of the maximum peak to the noise floor, so that the estimation result is highly correlated with the actual signal strength and does not depend on complex models; the noise statistics after excluding the peak value can avoid the deviation of the noise reference caused by local abnormal points or isolated interference, making the SNR estimation more robust.

[0089] Further, after obtaining the signal-to-noise ratio, normalizing the first correlation peak matrix and the second correlation peak matrix includes: The noise floor value is taken as a normalization reference, and the value of each cell in the first correlation peak matrix is quantized by the same noise reference.

[0090] The correlation output of all matrix cells in the first correlation peak matrix is adjusted in amplitude one by one by taking the noise floor value as a scaling factor, so that the numerical value of each matrix cell reflects the energy ratio relative to the noise background, thereby forming the first normalized correlation peak matrix.

[0091] The second correlation peak matrix is processed by the corresponding noise floor value acquisition and amplitude normalization in the same way as the first correlation peak matrix to obtain the second normalized correlation peak matrix.

[0092] Specifically, correlation peak normalization is a key step, (1ms) and (10ms) are generated under different integration times and different noise bandwidths, and their peak amplitudes and noise floors are not comparable. Before fusion, they must be normalized to the same dimension. The peak-to-average ratio (PAR) is used for normalization in this embodiment: Calculate the noise floor mean value . .

[0093] Generate the first normalized matrix .

[0094] Generate the second normalized matrix .

[0095] At this time, and are dimensionless peak-to-average ratio matrices and can be fused by weighting.

[0096] Further, in step S5, generating the first adaptive weight coefficient includes steps E1-E4: E1: According to the pre-set signal-to-noise ratio switching center point parameter and slope gain parameter, a nonlinear mapping model for describing the change relationship of the weight with the signal-to-noise ratio is constructed.

[0097] E2: The signal-to-noise ratio estimate value obtained based on the first correlation peak matrix is input into the nonlinear mapping model.

[0098] E3: After completing the nonlinear mapping, the numerical value of the mapping output is extracted as the first adaptive weight coefficient.

[0099] E4: The second adaptive weight coefficient is obtained by subtracting 1 from the first adaptive weight coefficient.

[0100] In the embodiment of the present application, in step E1, the nonlinear mapping model adopts a nonlinear mapping model with S-shaped variation characteristics, including the following steps E111-E113: E111: According to the preset signal-to-noise ratio switching center point parameter and the slope gain parameter, a nonlinear mapping model with S-shaped variation characteristics (such as Sigmoid function) is constructed, so that the model can quickly output a weight close to one when the signal-to-noise ratio is higher than the center point, and output a weight close to zero when the signal-to-noise ratio is lower than the center point, and maintain smooth transition in the critical region to avoid the weight jumping sharply due to slight jitter of the signal-to-noise ratio.

[0101] E112: The signal-to-noise ratio value obtained by the signal-to-noise ratio estimation step is input into the mapping model, and through the nonlinear compression and smooth change mechanism inside the model, the input signal-to-noise ratio is converted into a continuous weight ratio that can reflect its strength, and is naturally mapped to the range of zero to one.

[0102] E113: The output value of the mapping model is used as the first adaptive weight coefficient of the first frequency channel, and the coefficient is used to dynamically adjust the contribution of the first correlation peak matrix in the subsequent weighted fusion process, so as to realize adaptive emphasis or weakening of the first frequency signal in different signal-to-noise ratio environments.

[0103] Specifically, the obtained is substituted into the following nonlinear mapping function to calculate the first adaptive weight coefficient (namely, the weight of the B1I channel): wherein, is an activation function (such as Sigmoid function); is a center point parameter: this is a signal-to-noise ratio switching center point which can be preset by those skilled in the art according to the performance of the module, in the embodiment, the capture threshold (weak signal threshold) of B1I (1ms) is about 31 dB-Hz, and the capture threshold of B1C (10ms) is about 21 dB-Hz, can be set between the two, for example dB-Hz; k is a slope parameter: this parameter controls the smoothness of the fusion, the larger k is, the steeper the curve is, and the closer to the hard switching it is; the smaller k is, the wider the transition band is, in the embodiment, k can be selected as ; e is a natural constant.

[0104] The second adaptive weight coefficient (weight of the B1C channel) is calculated: In an alternative embodiment, the nonlinear mapping model can also employ a Tanh (hyperbolic tangent) nonlinear mapping, including the following steps E121-E123: E121: Construct a nonlinear mapping model based on a hyperbolic tangent variation curve, set the center interval and slope interval related to the signal-to-noise ratio variation, so that the model can maintain a wide smooth transition band near the medium signal-to-noise ratio, and tend to one and zero in the high signal-to-noise ratio and low signal-to-noise ratio regions respectively, thereby enhancing the stability of the weight variation in the critical region.

[0105] E122: Input the signal-to-noise ratio estimate into the hyperbolic tangent model, so that the input value is compressed to a limited range under the nonlinear mapping of the model, and a continuous output reflecting the strength of the signal-to-noise ratio is generated through the symmetry and asymptotic characteristics of the model, so that the output can naturally fall within the range of zero to one.

[0106] E123: Use the nonlinear mapping output as the first adaptive weight coefficient, so that the weight can gradually reduce the contribution of the first frequency point in the weak signal case, and gradually increase its contribution in the strong signal case, to ensure the stability and robustness of the weighted fusion in different signal environments.

[0107] In another alternative embodiment, the nonlinear mapping model can also employ a piecewise linear approximation model, including the following steps E131-E133: E131: Construct a piecewise linear mapping model based on a combination of multiple linear intervals, set the slope and boundary of the low signal-to-noise ratio interval, the transition interval and the high signal-to-noise ratio interval, so that the model can output a lower weight in the low signal-to-noise ratio stage, output a higher weight in the high signal-to-noise ratio stage, and realize gradual linear variation in the middle interval, to form an approximation to the S-shaped curve.

[0108] E132: Input the estimated signal-to-noise ratio value into the piecewise linear model, so that the model performs corresponding linear function mapping according to the interval into which the input falls, thereby generating a continuously changing weight value with simple calculation characteristics, for adapting to different signal-to-noise ratio environments.

[0109] E133: Use the value obtained by the piecewise linear mapping as the first adaptive weight coefficient, so that the coefficient obtains a dynamic adjustment effect close to the nonlinear curve at a low calculation cost, thereby realizing adaptive weighting of the first frequency point channel on a resource-limited platform.

[0110] It should be noted that the present application realizes the adaptive performance of "getting both fish and bear's paw": strong signal ( ): Sigmoid function output , the fusion decision matrix automatically focuses on the results of the first frequency point (1ms), realizing fast capture in strong signal; weak signal ( ): Sigmoid function output The fusion decision matrix automatically focuses on the results of the second frequency point (10 ms), achieving high-sensitivity capture under weak signals.

[0111] Further, in step S6, the two obtained normalized matrices are weighted and summed to generate a final fusion decision matrix : Further, in step S7, the code phase and Doppler shift corresponding to the maximum peak value are output, including the following steps G1-G3: G1: The fusion decision matrix obtained after weighting is scanned, and the global maximum value of the correlation output is identified as the maximum peak value in the matrix area covering all code phase and Doppler shift combinations. The index position corresponding to the maximum peak value is obtained, which reflects the strongest matching point after fusion, and is used as the parameter basis for the candidate capture target.

[0112] G2: The maximum peak value is compared with the preset fusion decision threshold. When the maximum peak value exceeds the threshold in energy, it is determined that there is a peak response representing an effective satellite signal, and it is determined that the current digital intermediate frequency signal contains a satellite signal that can be captured, thereby completing the confirmation of the capture state.

[0113] G3: After confirming the successful capture, the code phase index value and the Doppler shift index value are extracted from the matrix index corresponding to the maximum peak value, and the two indexes are converted into actual coarse code phase estimation and coarse Doppler shift estimation, and the estimation results are used as the initial conditions for entering the tracking loop.

[0114] Specifically, by traversing all corresponding (code phase, frequency shift) search units in the two matrices, finally, the global maximum peak value is searched in the matrix , a fusion decision threshold (e.g. , i.e., the peak-to-average ratio after fusion reaches 5).

[0115] If : announce successful capture, the index of the maximum peak value is the coarse estimated code phase and Doppler shift, and this parameter is passed to the tracking loop.

[0116] If : ​Declare the capture failure, the system enters the next capture cycle (for example, search the next satellite, or wait for the new data block).

[0117] Embodiment 3, which is different from the first two embodiments, is a third embodiment of the application, which is an adaptive dual-frequency joint acquisition system based on signal-to-noise ratio estimation, comprising an acquisition module, a correlation peak matrix calculation module, a signal-to-noise ratio calculation module, a weighting module and an acquisition module; the acquisition module acquires a digital intermediate frequency signal containing a first frequency signal and a second frequency signal; the correlation peak matrix calculation module performs parallel correlation processing on the first frequency signal in the digital intermediate frequency signal to generate a first correlation peak matrix using a first coherent integration time, and performs parallel correlation processing on the second frequency signal in the digital intermediate frequency signal to generate a second correlation peak matrix using a second coherent integration time, the second coherent integration time being greater than the first coherent integration time; the signal-to-noise ratio calculation module estimates the signal-to-noise ratio of the digital intermediate frequency signal based on the first correlation peak matrix; the weighting module inputs the signal-to-noise ratio into a nonlinear mapping function to generate a first adaptive weight coefficient, determines a second adaptive weight coefficient based on the first adaptive weight coefficient, and applies the first adaptive weight coefficient and the second adaptive weight coefficient to the corresponding search units in the first correlation peak matrix and the second correlation peak matrix, respectively, for weighted fusion to generate a fusion decision matrix; the acquisition module searches for the maximum peak value in the fusion decision matrix, and determines that the acquisition is successful when the maximum peak value exceeds a preset fusion decision threshold, and outputs the code phase and the Doppler frequency shift corresponding to the maximum peak value.

[0118] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the embodiments of the application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various storage program codes.

[0119] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.

[0120] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer.

[0121] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, which are known in the art, can be used: a combination of discrete logic circuits having logic gates for implementing logic functions upon data signals, application specific integrated circuits having logic gates, field programmable gate arrays (FPGA), or other components, which are expressly contemplated herein.

[0122] It should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not limit the present application, although the present application has been described in detail with reference to the preferred embodiments. Those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. An adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation, characterized in that: include, Acquire a digital intermediate frequency signal containing a first frequency signal and a second frequency signal; Using the first coherent integration time, parallel correlation processing is performed on the first frequency point signal in the digital intermediate frequency signal to generate a first correlation peak matrix; Using the second coherent integration duration, parallel correlation processing is performed on the second frequency point signal in the digital intermediate frequency signal to generate a second correlation peak matrix; The second coherent integration duration is greater than the first coherent integration duration, and the signal-to-noise ratio of the digital intermediate frequency signal is estimated based on the first correlation peak matrix; The signal-to-noise ratio is input into a nonlinear mapping function to generate a first adaptive weight coefficient, and a second adaptive weight coefficient is determined based on the first adaptive weight coefficient. For the corresponding search units in the first correlation peak matrix and the second correlation peak matrix, the first adaptive weight coefficient and the second adaptive weight coefficient are respectively applied to perform weighted fusion to generate a fusion decision matrix; The maximum peak value is searched in the fusion decision matrix. When the maximum peak value exceeds the preset fusion decision threshold, the acquisition is determined to be successful, and the code phase and Doppler frequency shift corresponding to the maximum peak value are output.

2. The adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in claim 1, characterized in that: The acquisition of the digital intermediate frequency signal containing the first frequency signal and the second frequency signal includes, The radio frequency front end receives radio frequency signals from the satellite and performs low-noise amplification processing on the radio frequency signals; Frequency domain selection and frequency mapping processing are performed on the low-noise amplified radio frequency signal to convert the radio frequency signal to the intermediate frequency channels corresponding to the first frequency point and the second frequency point, respectively, so as to obtain the analog intermediate frequency signal containing the energy of the target frequency point; The intermediate frequency channels of the first frequency point and the second frequency point are respectively subjected to analog-to-digital conversion to generate corresponding digital intermediate frequency sampling sequences, and the digital intermediate frequency sampling sequences are input into the baseband processor to form a digital intermediate frequency signal that simultaneously contains the first frequency point signal and the second frequency point signal.

3. The adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in claim 2, characterized in that: The generation of the first correlation peak matrix includes, The first frequency signal is divided into time slices according to the first coherent integration duration. The digital intermediate frequency data corresponding to each duration is sent to the local pseudocode generator and the local carrier generation module. Several sets of local reference signals are generated under different code phase and Doppler frequency shift conditions. The matching degree of the digital intermediate frequency signal and the local reference signal is calculated, and a correlation output result covering the search space of the first frequency point is generated within the first coherent integration time by using a correlation operation structure with parallel processing capability. The relevant output results are organized into a two-dimensional matrix according to the code phase index and Doppler index, so that each matrix unit corresponds to the correlation energy value under a specific code phase and a specific Doppler frequency shift, and thus form the first correlation peak matrix for the first frequency signal acquisition decision.

4. The adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in claim 3, characterized in that: Estimating the signal-to-noise ratio of the digital intermediate frequency signal based on the first correlation peak matrix includes: Extract the correlation output covering all code phases and Doppler combinations from the first correlation peak matrix, obtain the maximum correlation peak of the corresponding first frequency signal by searching the global maximum value in the matrix, and use it as a characterization of the effective signal energy in the current environment; Statistical analysis is performed on other matrix units in the first correlation peak matrix that do not contain the maximum correlation peak. By analyzing the correlation output of other matrix units in the region, representative values ​​reflecting the background noise level are extracted and used as the noise base value of the current search space. The peak-to-noise ratio (PNR) metric is generated based on the ratio between the maximum correlation peak value and the noise floor value, and the PNR metric is converted into an estimation result in terms of signal-to-noise ratio (SNR) through a preset mapping relationship.

5. The adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in claim 4, characterized in that: After obtaining the signal-to-noise ratio, normalization of the first correlation peak matrix and the second correlation peak matrix includes: Using the noise baseline value as a normalization reference, the value of each cell in the first correlation peak matrix is ​​quantified by the same noise reference. Using the noise floor value as a scaling factor, the amplitude of the correlation output of all matrix units in the first correlation peak matrix is ​​adjusted one by one so that the value of each matrix unit reflects the energy ratio relative to the noise background, thereby forming the first normalized correlation peak matrix. Using the same method as the first correlation peak matrix, the second correlation peak matrix is ​​subjected to the corresponding noise floor value acquisition and amplitude normalization processing to obtain the second normalized correlation peak matrix.

6. The adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in claim 5, characterized in that: The generation of the first adaptive weight coefficients includes, Based on the pre-set signal-to-noise ratio switching center point parameters and slope gain parameters, a nonlinear mapping model is constructed to describe the relationship between weight and signal-to-noise ratio. The signal-to-noise ratio estimate obtained based on the first correlation peak matrix is ​​fed into the nonlinear mapping model as input. After completing the nonlinear mapping, the numerical value of the mapping output is extracted as the first adaptive weight coefficient. Subtract the first adaptive weight coefficient from 1 to obtain the second adaptive weight coefficient.

7. The adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in claim 6, characterized in that: The output of the code phase and Doppler frequency shift corresponding to the maximum peak value includes, The fusion decision matrix obtained after weighted fusion is traversed and scanned. The global maximum value of the relevant output is identified as the maximum peak value in the matrix region covering all code phase and Doppler frequency shift combinations, and the index position corresponding to the maximum peak value is obtained. The maximum peak value is compared with a preset fusion decision threshold. When the maximum peak value exceeds the threshold in terms of energy, it is determined that there is a peak response that reflects the existence of a valid satellite signal, and it is determined that the current digital intermediate frequency signal contains a captureable satellite signal, thereby completing the confirmation of the capture status. After confirming successful acquisition, the code phase index value and Doppler frequency shift index value are extracted from the matrix index corresponding to the maximum peak value. The two indices are converted into actual coarse code phase estimation and coarse Doppler frequency shift estimation, and the estimation results are used as the initial conditions for entering the tracking loop.

8. An adaptive dual-frequency joint acquisition system based on signal-to-noise ratio estimation, employing the adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in any one of claims 1 to 7, characterized in that: It includes an acquisition module, a correlation peak matrix calculation module, a signal-to-noise ratio calculation module, a weighting module, and a capture module; The acquisition module acquires a digital intermediate frequency signal containing a first frequency signal and a second frequency signal; The correlation peak matrix calculation module uses a first coherent integration duration to perform parallel correlation processing on the first frequency point signal in the digital intermediate frequency signal to generate a first correlation peak matrix, and uses a second coherent integration duration to perform parallel correlation processing on the second frequency point signal in the digital intermediate frequency signal to generate a second correlation peak matrix, wherein the second coherent integration duration is longer than the first coherent integration duration. The signal-to-noise ratio calculation module estimates the signal-to-noise ratio of the digital intermediate frequency signal based on the first correlation peak matrix; The weighting module inputs the signal-to-noise ratio into a nonlinear mapping function to generate a first adaptive weighting coefficient, determines a second adaptive weighting coefficient based on the first adaptive weighting coefficient, and applies the first adaptive weighting coefficient and the second adaptive weighting coefficient to the corresponding search units in the first correlation peak matrix and the second correlation peak matrix respectively to perform weighted fusion to generate a fusion decision matrix. The acquisition module searches for the maximum peak value in the fusion decision matrix. When the maximum peak value exceeds a preset fusion decision threshold, the acquisition is determined to be successful, and the code phase and Doppler frequency shift corresponding to the maximum peak value are output.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the adaptive dual-frequency joint acquisition method based on signal-to-noise ratio estimation as described in any one of claims 1 to 7.

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