Non-cooperative signal carrier frequency estimation method and system in motion state

Abstract

The invention discloses a non-cooperative signal carrier frequency estimation method and system in a motion state, and relates to the field of wireless communication and signal processing, and the method comprises the following steps: selecting an energy prominent frequency point in a frequency spectrum of a non-cooperative signal, and combining the resolution of the frequency spectrum to obtain a carrier frequency coarse estimation value; carrying out frequency mixing down-sampling processing, windowing and nonlinear transformation on the non-cooperative signal, carrying out time-frequency conversion, and utilizing a DFT spectrum peak decimal correction method to obtain a frequency fine estimation value to compensate the carrier frequency coarse estimation value so as to obtain a carrier frequency fine estimation value; selecting an observation time period, and calculating a carrier phase observation value according to the carrier frequency fine estimation value; and after the carrier phase observation value is compensated according to the motion phase compensation item and is represented as a standard linear regression form, calculating the carrier frequency deviation to compensate the carrier frequency fine estimation value so as to obtain an accurate carrier frequency estimation value. According to the invention, the problem that the carrier frequency estimation deviation of the non-cooperative signal in the motion state is increased can be solved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and signal processing, specifically to a method and system for estimating the carrier frequency of non-cooperative signals in motion. Background Technology

[0002] In the fields of wireless communication and signal processing, accurate estimation of key parameters such as carrier frequency and carrier phase of the received signal is a fundamental step in signal acquisition, synchronization, demodulation, and subsequent parameter analysis. In non-cooperative reception scenarios, the receiver cannot obtain prior knowledge such as synchronization information, initial carrier phase, or relatively accurate carrier frequency from the transmitter; it can only rely on the received signal itself to perform parameter inversion. Therefore, the accuracy and stability of carrier frequency estimation directly affect the performance of the entire system, becoming one of the core technical problems urgently needing to be solved in non-cooperative communication. In existing technologies, besides being used for communication demodulation, the carrier frequency and phase parameters of non-cooperative signals can also serve as important observables for applications such as wireless positioning, ranging, or motion sensing. For example, by analyzing the phase change or frequency shift of the received signal, the relative distance change between the receiver and the signal transmitter can be inverted, thus providing support for long-wavelength positioning. Therefore, the estimation accuracy of carrier frequency and phase parameters not only directly affects communication performance but also has a significant impact on the accuracy of signal parameter-based positioning and sensing.

[0003] Under stationary reception conditions, the relative position between the receiver and the signal transmitter remains constant. In this case, the carrier frequency can be effectively estimated using a phase-locked loop (PLL), a frequency tracking loop, or a phase observation-based processing method. In such scenarios, the signal propagation path is stable, and the carrier phase changes approximately linearly with time. Therefore, frequency estimation algorithms based on linear phase models are suitable and typically achieve high estimation accuracy. However, when the receiver is in motion, its relative motion with the transmitter causes dynamic changes in the signal propagation distance, introducing motion-related Doppler shifts and additional phase modulation into the received signal. This motion-induced phase change is often coupled with carrier frequency deviation, and non-uniform motion causes the phase change of the received signal to exhibit non-linear characteristics. In this situation, traditional carrier frequency estimation methods based on linear phase assumptions are difficult to apply, and estimation accuracy and stability decrease significantly. To address the frequency estimation problem under motion conditions, some current techniques attempt to introduce motion models or Doppler compensation mechanisms into the estimation process. For example, compensation can be made by assuming the receiver moves at a known speed or along an ideal trajectory; or the influence of motion phase can be reduced by extending the observation time or improving the signal-to-noise ratio. However, most of the above methods rely on idealized motion models, which are difficult to adapt to complex or non-uniform motion scenarios.

[0004] Some existing technical solutions focus on Minimum Shift Keying (MSK) signals and estimate their carrier frequencies. For example, Chinese invention patent application CN114531329A discloses a carrier frequency estimation method that improves frequency estimation accuracy by stepping through statistical phase estimation results. This method gradually improves frequency estimation accuracy over continuous observation time by filtering and accumulating the phase estimation results. Another example is Chinese patent application CN116405136A, which discloses a carrier frequency estimation method that fits the frequency offset using the least squares method. This method uses conventional least squares to linearly fit the frequency deviation caused by phase rotation over an observation period to achieve high-precision frequency estimation. However, these methods estimate the carrier frequency under the condition that the receiver and the signal transmitter are relatively stationary. While they can achieve a certain level of accuracy under these assumptions, when the receiver enters a moving state and needs to quickly lock onto newly entered non-cooperative signals, the carrier frequency estimation methods based on stationary conditions are difficult to apply directly. This can easily lead to increased frequency estimation deviation, prolonged locking time, and even problems with unstable signal locking. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for estimating the carrier frequency of non-cooperative signals in motion, so as to solve the problem of increased deviation in carrier frequency estimation of non-cooperative signals in motion in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect is a method for estimating the carrier frequency of non-cooperative signals under motion conditions, which includes the following steps: Obtain the non-cooperative signal of the base station to be locked, select the energy prominent frequency point in the spectrum of the non-cooperative signal, and obtain a coarse estimate of the carrier frequency by combining the resolution of the spectrum. The non-cooperative signal is downsampled and mixed based on the coarse estimate of the carrier frequency to obtain the downsampled intermediate frequency (IF) signal. The downsampled IF signal is then windowed and nonlinearly transformed to obtain a single spectrum from the time domain to the frequency domain. Based on the single spectrum and using the DFT spectral peak fractional correction method, a fine estimate of the frequency of the downsampled IF signal is obtained. The coarse estimate of the carrier frequency is then compensated using the fine estimate to obtain a fine estimate of the carrier frequency. The observation time period of the non-cooperative signal is selected. Based on the fine estimate of the carrier frequency, the non-cooperative signal within the observation time period is sequentially subjected to mixing downsampling, windowing processing and nonlinear transformation. The carrier phase observation value is then calculated from the time domain to the frequency domain. The transmit / receive distance between the receiver and the base station to be locked is obtained, and a motion phase compensation term is constructed based on the transmit / receive distance and a fine estimate of the carrier frequency. The carrier phase observation value is then compensated based on the motion phase compensation term to obtain the compensated phase sequence. After the compensated phase sequence is expressed in the standard linear regression form, the carrier frequency deviation is calculated using the recursive least squares method. The carrier frequency deviation is then used to compensate the fine estimate of the carrier frequency to obtain the accurate carrier frequency estimate of the non-cooperative signal within the observation period.

[0007] In some implementations, selecting energy-prominent frequencies within the spectrum of the non-cooperative signal, and considering the resolution of the spectrum, includes: After applying a Blackman window to the non-cooperative signal, the non-cooperative signal is converted from the time domain to the frequency domain by a short-time Fourier transform. The spectral energy in the frequency domain is sorted, and the energy prominence frequency points with spectral energy greater than a preset prominence value are selected as signals of interest. The resolution of the spectrum is calculated based on the length of the Blackman window and the sampling rate of the non-cooperative signal; Multiplying the energy prominence frequency point by the resolution of the spectrum yields a coarse estimate of the carrier frequency.

[0008] In some implementations, converting the downsampled intermediate frequency signal from the time domain to the frequency domain to obtain a single spectrum after windowing and nonlinear transformation includes: applying a Blackman window to the downsampled intermediate frequency signal, performing a square transform on the windowed downsampled intermediate frequency signal, and then obtaining a single spectrum through a discrete Fourier transform.

[0009] In some implementations, based on the single spectrum and using the DFT spectral peak fractional correction method, a precise frequency estimate of the downsampled intermediate frequency signal is obtained, including: Search for spectral peaks and their adjacent spectral lines in the single spectrum to obtain the corresponding discrete Fourier transform complex spectrum values; Calculate the fractional offset based on the discrete Fourier transform complex spectrum value; Based on the frequency index corresponding to the spectral peak and the fractional offset, the precise frequency estimate of the downsampled intermediate frequency signal is obtained.

[0010] In some implementations, the carrier phase observation value is obtained by sequentially performing mixing downsampling, windowing, and nonlinear transformation on the non-cooperative signal within the observation period based on the fine estimate of the carrier frequency, and then converting from the time domain to the frequency domain. This includes: Based on the fine estimate of the carrier frequency, the non-cooperative signal within the observation period is sequentially subjected to mixing downsampling, windowing, and nonlinear transformation. Then, discrete Fourier transforms are performed at the first subcarrier frequency point and the second subcarrier frequency point to obtain the first complex spectrum value and the second complex spectrum value. The first subcarrier phase is obtained by performing an arctangent operation on the imaginary and real parts of the first complex spectrum value, and the second subcarrier phase is obtained by performing an arctangent operation on the imaginary and real parts of the second complex spectrum value. The average value of the first subcarrier phase and the second subcarrier phase is taken as the carrier phase observation value.

[0011] In some implementations, the transmit / receive distance between the receiver and the target base station is obtained, and a motion phase compensation term is constructed based on the transmit / receive distance and a fine estimate of the carrier frequency, including: The receiver's position information at each observation time within the observation period is obtained, and combined with the fixed position coordinates of the base station to be locked, the geometric distance between the receiver and the base station to be locked at each observation time is calculated as the transmission distance; Using a reference time within the observation period as a benchmark, calculate the distance increment of the geometric distance at each observation time relative to the geometric distance at the reference time; Based on the distance increment and the fine estimate of the carrier frequency, a motion phase compensation term is constructed.

[0012] In some implementations, based on the compensated phase sequence expressed in a standard linear regression form, the carrier frequency deviation is calculated using a recursive least squares method, including: A linear observation form of the compensated phase sequence is established within the observation period, and the linear observation form is converted into a standard linear regression form; the linear observation form includes a residual frequency offset term; The standard linear regression form is recursively estimated using the recursive least squares method to obtain the parameter vector at each observation time. The residual frequency offset term in the parameter vector at the last observation time is taken as the carrier frequency offset for the current observation period.

[0013] Secondly, a non-cooperative signal carrier frequency estimation system under motion conditions includes: The carrier frequency coarse estimation calculation module is used to obtain the non-cooperative signal of the base station to be locked, select the energy prominent frequency point in the spectrum of the non-cooperative signal, and obtain the coarse estimation value of the carrier frequency by combining the resolution of the spectrum. The carrier frequency fine estimate calculation module is used to perform mixing and downsampling processing on the non-cooperative signal based on the coarse carrier frequency estimate to obtain a downsampled intermediate frequency signal. After windowing and nonlinear transformation of the downsampled intermediate frequency signal, it is converted from the time domain to the frequency domain to obtain a single spectrum. Based on the single spectrum and using the DFT spectral peak fractional correction method, the fine estimate of the frequency of the downsampled intermediate frequency signal is obtained. The fine estimate of the frequency is used to compensate the coarse carrier frequency estimate to obtain the fine carrier frequency estimate. The carrier phase observation calculation module is used to select the observation time period of the non-cooperative signal, and perform mixing downsampling, windowing and nonlinear transformation on the non-cooperative signal within the observation time period according to the carrier frequency fine estimate, and then calculate the carrier phase observation value from the time domain to the frequency domain. The motion phase compensation module is used to obtain the transmission distance between the receiver and the base station to be locked, and to construct a motion phase compensation term based on the transmission distance and the carrier frequency fine estimate. The carrier phase observation value is compensated based on the motion phase compensation term to obtain the compensated phase sequence. The carrier frequency accurate estimation module is used to calculate the carrier frequency deviation using the recursive least squares method after the compensated phase sequence is expressed in a standard linear regression form, and to compensate the carrier frequency fine estimate value according to the carrier frequency deviation to obtain the accurate carrier frequency estimate value of the non-cooperative signal within the observation period.

[0014] Thirdly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the non-cooperative signal carrier frequency estimation method under motion conditions.

[0015] Fourthly, a computer program product comprising a computer program, characterized in that, when executed by a processor, the computer program implements the non-cooperative signal carrier frequency estimation method under the motion state.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for estimating the carrier frequency of non-cooperative signals in motion. After acquiring the non-cooperative signal in motion, the method selects the energy-prominent frequency points through spectral analysis and combines this with spectral resolution to obtain a coarse estimate of the carrier frequency, solving the problem of difficulty in quickly locating unknown signals in non-cooperative scenarios. Based on the coarse estimate, the signal is mixed and downsampled to obtain an intermediate frequency (IF) signal, reducing the data rate for subsequent fine processing. Windowing is applied to the IF signal to suppress interference spectrum leakage. After nonlinear transformation, the signal is converted to the frequency domain to obtain a single spectrum, making the carrier-related spectral lines clearly visible. The DFT peak fractional correction method overcomes the resolution limitation of the Discrete Fourier Transform (DFT) to obtain a high-precision fine estimate of the frequency. This fine estimate is used to compensate for the coarse estimate to obtain a fine estimate of the carrier frequency, providing a high-precision initial frequency reference for subsequent processing. After selecting an observation period, the signal is mixed, downsampled, windowed, and nonlinearly transformed again based on the fine estimate, converting the signal to the frequency domain to calculate the carrier phase observation, realizing the transition from frequency estimation to phase extraction. This method obtains the transmit / receive distance between the receiver and the base station and constructs a motion phase compensation term based on the transmit / receive distance and a fine estimate. This compensation term is used to compensate the phase observations, resulting in a compensated phase sequence. By introducing positioning information, real-time quantization is achieved, eliminating nonlinear phase changes caused by receiver motion and converting the nonlinear phase into a linearized sequence. The compensated phase sequence is expressed in a standard linear regression form, and the carrier frequency deviation is calculated using recursive least squares. A recursive mechanism is used to optimize the residual frequency deviation estimate point by point, and this deviation is used to compensate the fine estimate, resulting in an accurate carrier frequency estimate. This method eliminates nonlinear phase disturbances introduced by motion through positioning-assisted compensation and achieves high-precision tracking of the residual frequency deviation by combining the recursive estimation capability of recursive least squares. It solves the problem that existing technologies, relying on linear phase assumptions, cannot handle nonlinear phase changes under motion conditions, leading to increased carrier frequency estimation deviations. This method achieves high-precision carrier frequency locking for non-cooperative signals in motion scenarios.

[0017] Furthermore, by applying a Blackman window to the non-cooperative signal and then using a short-time Fourier transform to convert the signal from the time domain to the frequency domain, spectral leakage of strong interference signals over a wide bandwidth can be effectively suppressed. This prevents weak target signals from being overwhelmed by interference tails in the time spectrum, improving the accuracy of selecting energy-prominent frequency points. The spectral energy in the frequency domain is sorted, and frequency points greater than a preset prominence value are selected as signals of interest. By searching for signals in the two-dimensional time and frequency plane using a short-time Fourier transform, bursty or discontinuous non-cooperative signals can be captured, avoiding signal omissions caused by the loss of time information in ordinary Fourier transforms. The spectral resolution is calculated based on the length of the Blackman window and the sampling rate of the non-cooperative signal. Multiplying the energy-prominent frequency points by the resolution yields a coarse estimate of the carrier frequency, which provides a reliable frequency reference for subsequent fine processing.

[0018] Furthermore, a Blackman window is used to window the downsampled intermediate frequency signal before performing a square transform. This suppresses spectral leakage of interference signals that may remain after mixing and downsampling, reduces the noise floor, and makes the carrier-related spectral lines generated after the square transform cleaner and sharper, enhancing the distinguishability of the spectral lines. The square-transformed signal is then converted to the frequency domain using a discrete Fourier transform to obtain a single spectrum, yielding high-resolution spectral data. This provides high-quality spectral line input for the subsequent DFT spectral peak fractional correction method, ensuring the accuracy of frequency estimation and avoiding correction failure due to interference spectral line contamination.

[0019] Furthermore, spectral peaks and their adjacent spectral lines within a single spectrum are searched to obtain the corresponding Discrete Fourier Transform (DFT) complex spectrum values. The fractional offset is calculated using the amplitude and phase information contained in the complex spectrum values, overcoming the inherent limitations of the DFT's frequency resolution and significantly improving frequency estimation accuracy. Based on the frequency index corresponding to the spectral peak and the fractional offset, a precise frequency estimate of the downsampled intermediate frequency signal is obtained. This precise estimate serves as the initial frequency reference for subsequent motion compensation and RLS recursive estimation, preventing subsequent compensation and recursion from failing to converge due to excessive initial frequency deviation.

[0020] Furthermore, the average value of the first subcarrier phase and the second subcarrier phase is used as the carrier phase observation value. By averaging the two phases, the random error and noise impact of the single subcarrier phase estimation are effectively reduced, the robustness and accuracy of the carrier phase observation value are improved, and a more reliable phase input is provided for subsequent motion phase compensation.

[0021] Furthermore, using the reference time within the observation period as a benchmark, the distance increment of the geometric distance at each observation time relative to the reference time is calculated. This eliminates the influence of absolute distance on phase compensation, retaining only the relative distance change caused by motion. A motion phase compensation term is constructed based on the distance increment and a fine estimate of the carrier frequency, accurately quantifying the phase change caused by receiver motion. This provides an accurate compensation basis for eliminating the nonlinear disturbance of motion on the carrier phase observations, resulting in a linear phase sequence after compensation, satisfying the processing prerequisites of the subsequent RLS algorithm. Attached Figure Description

[0022] Figure 1 A flowchart of a non-cooperative signal carrier frequency estimation method under motion conditions provided in an embodiment of the present invention; Figure 2 This is a structural diagram of a non-cooperative signal carrier frequency estimation system under motion conditions provided in an embodiment of the present invention; Figure 3 This is a hardware block diagram of the non-cooperative signal carrier frequency estimation system in a mobile state according to an embodiment of the present invention; Figure 4A flowchart illustrating the stages of a non-cooperative signal carrier frequency estimation method under motion conditions provided in an embodiment of the present invention; Figure 5 The windowed STFT spectrum comparison diagrams of the non-cooperative signal carrier frequency estimation method under motion state provided in the embodiments of the present invention are shown, where (a) is the rectangular window STFT spectrum diagram and (b) is the Blackman window STFT spectrum diagram. Figure 6 The diagram shows the interference suppression effect of the non-cooperative signal carrier frequency estimation method under motion state provided in the embodiment of the present invention, where (a) is the square spectrum without window processing and (b) is the square spectrum with window optimization. Figure 7 A flowchart illustrating the frequency offset estimation based on positioning results for a non-cooperative signal carrier frequency estimation method under motion conditions, as provided in an embodiment of the present invention. Figure 8 The simulation trajectory diagram provided for the embodiments of the present invention; Figure 9 The motion phase diagram provided in this embodiment of the invention is as follows: (a) is the Doppler phase calculated from the real-time distance between the receiver and the base station, and (b) is the motion phase compensation term constructed based on the NWLS / INS positioning solution. Figure 10 This is a phase motion compensation and curve fitting diagram provided in an embodiment of the present invention; Figure 11 This is an RLS carrier frequency stepping diagram provided in an embodiment of the present invention; Figure 12 The figures show the simulation results of carrier frequency estimation accuracy under different motion platforms provided in the embodiments of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. The content is for explanation rather than limitation of the present invention.

[0024] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification of this invention are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, systems, products or devices.

[0025] This embodiment uses the MSK signal as an example to specifically illustrate the method and system for estimating the carrier frequency of non-cooperative signals under motion conditions proposed in this invention. The invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0026] like Figure 3 The hardware block diagram of the non-cooperative signal carrier frequency estimation system under motion state of the present invention includes an antenna, a signal conditioning circuit, an analog-to-digital conversion module, a processing unit based on ZYNQ 7z045, and a host computer. First, the antenna receives spatial electromagnetic signals and outputs them to the conditioning circuit. The signal conditioning circuit performs filtering and variable gain control on the received analog signals. Subsequently, the analog-to-digital conversion module samples and quantizes the conditioned analog signals, outputs the corresponding digital sampling sequence, and sends the digital sequence to the programmable PL terminal of the ZYNQ 7z045. The ZYNQ 7z045 PL terminal constitutes a digital signal processing module, which performs time-frequency domain processing and feature extraction operations related to carrier frequency estimation. The ZYNQ 7z045 PS terminal is composed of an ARM core. On the one hand, it receives the observations estimated by the PL terminal and performs the control and update required for carrier frequency estimation. On the other hand, the PS terminal contains a positioning solution module for obtaining the combined positioning results. Finally, the host computer communicates bidirectionally with the ZYNQ 7z045 PS terminal for configuring and monitoring the system, and displaying, recording, and interacting with the estimation results.

[0027] like Figure 1 and Figure 4 As shown, this embodiment provides a method for estimating the carrier frequency of a non-cooperative signal under motion conditions, including the following steps: Carrier frequency acquisition phase (including S1 and S2): S1, acquire the non-cooperative signal of the base station to be locked, select the energy prominent frequency point in the spectrum of the non-cooperative signal, and obtain a coarse estimate of the carrier frequency by combining the resolution of the spectrum; S1.1, the newly acquired non-cooperative MSK signal from the analog-to-digital conversion module. Spectral analysis and blind estimation are performed. A non-cooperative spectrum sensing method based on energy detection is used to transform the MSK signal from the time domain to the frequency domain. The spectral energy is sorted, and the frequency points with prominent energy are selected as the MSK signals of interest. A window function is used to transform the time-domain signal... Divide into multiple segments, and then perform a Fourier transform on each segment separately; S1.2, the broadband frequency band of this system may contain strong single-tone interference signals, requiring a window with high amplitude recognition accuracy to detect weak components in strong signals. Therefore, this invention uses the Blackman window as the window function, and the expression of the Blackman window function is as follows: (1) Where n is the number of sample points and N is the length of the window, which is also the number of STFT points. Figure 5 The simulation results are for a rectangular window STFT and a Blackman window STFT under strong interference signals, respectively. It can be seen that the Blackman window STFT can separate the MSK signal. The sampling rate is [missing information]. The spectral resolution of the MSK signal is: (2) S1.3, the frequency points of the MSK signal of interest found during the search. With spectral resolution Multiplying them together gives a coarse estimate of the carrier frequency. : (3) S2, based on the coarse estimate of the carrier frequency, the non-cooperative signal is mixed and downsampled to obtain the downsampled intermediate frequency signal. After windowing and nonlinear transformation, the downsampled intermediate frequency signal is converted from the time domain to the frequency domain to obtain a single spectrum. Based on the single spectrum and using the DFT spectral peak fractional correction method, the fine estimate of the frequency of the downsampled intermediate frequency signal is obtained. The fine estimate of the frequency is used to compensate the coarse estimate of the carrier frequency to obtain the fine estimate of the carrier frequency. S2.1, using this coarse estimate to evaluate the MSK signal. Downsampling and frequency conversion processing are necessary to suppress large interferences and in-band Gaussian white noise. Therefore, this invention chooses to perform multiple decimation filtering on the MSK signal to be locked to reduce the sampling rate to [a specific value]. At the same time, the spectrum will be shifted to a fixed low-intermediate frequency. .

[0028] S2.2, In order to suppress the weakening of the MSK signal spectral characteristics caused by residual interference spectral leakage, the downsampled intermediate frequency MSK signal... A Blackman window is added to suppress spectral leakage from residual interference and reduce the noise floor. Figure 6 The windowing interference suppression effect is demonstrated; the MSK signal after windowing is clearly visible. The two spectral lines after being squared.

[0029] S2.3, based on this, the present invention selects to use the DFT spectral peak fractional correction method to achieve fine estimation of carrier frequency. First, the intermediate frequency MSK signal with Blackman window is subjected to... The signal is obtained by performing a square transformation. .

[0030] S2.4, for Perform full point The discrete Fourier transform (DFT) of a point, and the search For each of the two spectral peaks in the spectrum, record the corresponding peak index. and These correspond to two subcarriers respectively. Since a full-point DFT is performed, then... Spectral resolution It should be 1Hz, as shown below: (4) At this time, the MSK signal symbol rate Exactly equal to The distance between two spectral peak indices is represented as follows: (5) S2.5, signal Two subcarrier frequencies The estimated value is: (6) in, For signal The two spectral peak indices. It is a decimal offset that satisfies: (7) in, Indicates signal In the DFT complex spectrum values ​​at each frequency point , These are complex spectrum samples taken from one and two frequency points adjacent to the left and right of the main spectral peak, respectively. To take the real part of a complex number; coefficient , , , , These are constants used to match the local model of the spectral lines.

[0031] S2.6, Intermediate Frequency MSK Signal Carrier frequency estimate for: (8) S2.7, the frequency offset compensation value is obtained by the following formula: (9) S2.8, using this frequency offset compensation value to adjust the initially estimated carrier frequency. Compensation is performed to obtain a fine estimate of the carrier frequency of the MSK signal. : (10) Positioning-assisted phase correction phase (including S3 and S4): S3, Select the observation time period of the non-cooperative signal, and perform mixing downsampling, windowing and nonlinear transformation on the non-cooperative signal within the observation time period according to the fine estimate of the carrier frequency, and then convert from the time domain to the frequency domain to calculate the carrier phase observation value. S3.1, the receiver selects a relatively stable motion trajectory as the observation period, and records the MSK signal to be locked received every 1 second during this period. Using fine frequency estimation After frequency conversion and downsampling, the signal is obtained by squaring after windowing. .

[0032] S3.2, for At two subcarrier frequency points and Perform DFT at each location to obtain the complex spectrum. and : (11) in, For signal The number of sampling points.

[0033] S3.3, relative formula and Perform the arctangent operation to obtain the signal. Two subcarrier phases: (12) in, Frequency point To extract the imaginary part of the DFT result, Frequency point The real part of the DFT result.

[0034] S3.4, Obtain the receiver position estimation sequence of the NWLS / INS combined positioning output within the same observation period. : (13) in, , , These are the three-axis coordinate estimates of the receiver in the geocentric coordinate system.

[0035] S3.5, The coordinates of the transmitting base station are known and fixed: (14) in, , , These are the three-axis coordinate constants of the base station.

[0036] S3.6, Resample the positioning output to match the phase observation time. Align them to get: (15) in, Indicates the first Each phase observation time, This indicates the number of phase observation points within that time period. Indicates the first Each positioning output moment This is a resampling operator used to map the trajectory sequence on the positioning time axis to the phase observation time axis.

[0037] S3.7, Extract the phase of the two subcarriers of the received signal during the observation period. , The average value is taken as the phase observation value: (16) S4, obtain the transmission distance between the receiver and the base station to be locked, construct a motion phase compensation term based on the transmission distance and the carrier frequency fine estimate, and compensate the carrier phase observation value based on the motion phase compensation term to obtain the compensated phase sequence; S4.1, calculate the geometric distance from the receiver to the base station from the positioning output: (17) S4.2, with reference time Based on this, construct the distance increment: (18) S4.3, frequency estimation With distance increment Constructing motion phase compensation terms: (19) in It is the speed of light.

[0038] S4.4, for and conduct Periodic consistency processing: Selecting a benchmark make and will Press and The same phase continuity rule is expanded to the continuous domain.

[0039] S4.5 introduces gating decision for the compensation term. The phase increment of the compensation term is defined as follows: (20) and according to the threshold Given the gated variables: (twenty one) when At that time, the compensation extension from the previous time step is used: (twenty two) S4.6, using phase compensation term For the original phase observation sequence Compensation is performed to obtain the compensated phase sequence: (twenty three) in, This is the frequency offset term, representing the linear phase change caused by the residual frequency offset of the carrier frequency; For the equivalent initial phase, The residual term includes the compensated phase residual and phase observation noise caused by the positioning error. composition: (twenty four) Dynamic frequency RLS recursive estimation stage (including S5): S5. Based on the compensated phase sequence expressed in the standard linear regression form, the carrier frequency deviation is calculated using the recursive least squares method. The carrier frequency deviation is then used to compensate the fine estimate of the carrier frequency to obtain the accurate carrier frequency estimate of the non-cooperative signal within the observation period.

[0040] S5.1, the frequency offset term of the compensated phase can be expressed as: ,in For the base station transmission frequency to be estimated, a fine-grained frequency estimate is needed. The residual frequency offset. Then the compensated phase sequence represented by equation (23) can be rewritten as: (25) S5.2 establishes a linear observation form with compensated phase within the observation period. Define the relative time variable: (26) Then equation (25) is equivalently transformed into: (27) in, This transformation does not change the slope parameter. The estimation results are different, but the stability of the calculation can be significantly improved.

[0041] S5.3, further rewriting equation (27) in the standard linear regression form: (28) in, (29) S5.4, introduces Kalman gain Controlling the impact of new observation data on parameter updates: (30) Among them, matrix The covariance matrix represents the uncertainty of the current parameter estimation. This is the forgetting factor, used to determine the weight of older observations. The closer the value is to 1, the greater the weight given to historical observations. A value slightly less than 1 can enhance the tracking ability for slowly changing frequency offsets.

[0042] S5.5, Calculate the prior error : (31) S5.6, Next, the parameters to be updated are obtained using Kalman gain and prior error: (32) S5.7, update the covariance matrix using the updated parameters: (33) S5.8, the receiver, during the observation period... After iteration at each observation point, the final parameter estimation result is: (34) S5.9, take the residual frequency offset estimate of the last observation point as the frequency offset compensation amount for this observation segment: (35) S5.10, for detailed frequency estimation After performing frequency offset compensation, the dynamic carrier frequency estimation result for this observation segment is obtained: (36) In summary, after obtaining a fine estimate of the carrier frequency of the MSK signal of the base station to be locked, the process of using the NWLS / INS combined positioning results to assist in estimating the carrier frequency of the receiver in motion is as follows: Figure 7 As shown.

[0043] The simulation of this invention uses a planar environment as a basic assumption, where east and north are defined as positive directions. This setting helps to more clearly analyze the motion trajectory of the vehicle. To verify the performance of the method of this invention in complex dynamic environments, a simulation environment was constructed as follows: Figure 8 The receiver's trajectory is shown. The initial position of the receiver is set to (0, 0), represented by a red pentagram in the diagram. The blue solid line represents the receiver's true ideal trajectory, and the black solid line represents the NWLS / INS combined positioning estimated trajectory. The receiver moved for a total of 90 seconds. Initially, the receiver remained stationary at its initial position for 10 seconds; subsequently... The device undergoes a uniform acceleration northward in a linear motion for 20 seconds; after acceleration, the receiver enters... The observation window is indicated by the red highlighted line in the figure. During this period, the receiver first travels north at a constant speed in a straight line for 10 seconds, then makes a 5-second constant speed left turn, and finally turns to travel west at a constant speed in a straight line. This stage covers the switching between straight and curved motion and is used to test the algorithm's ability to track nonlinear phases. After the observation ends, the receiver continues to travel west, makes a 5-second constant speed right turn, resumes traveling north, and finally decelerates uniformly for 20 seconds to reach the destination, as shown by the black square in the figure.

[0044] Set the location of the base station to be locked to (-700e3, 800e3). Figure 9 The diagram illustrates the changes in motion phase over a selected 20-second observation interval. Figure (a) shows the Doppler phase calculated using the real-time distance between the receiver and the base station, with the curve exhibiting a distinct parabolic shape. This indicates that the signal contains not only a fixed carrier frequency offset but also a time-varying Doppler frequency shift caused by the receiver's non-uniform motion (such as turning or acceleration / deceleration). Figure (b) shows the motion phase compensation term constructed based on the NWLS / INS positioning solution. This allows for motion compensation of the original estimated phase within the observation interval, yielding observations containing only the frequency offset term. Figure 10 As shown, the black circles represent the measured phase that changes non-linearly over time, the black straight lines represent the least squares fitted straight lines, and the red triangles represent the phase after motion compensation using NWLS / INS positioning data. It can be seen that the non-linear trend has been eliminated, and the phase points are approximately distributed on a straight line, as shown by the red solid line in the figure. This indicates that the compensated phase satisfies the linear model assumption and can be used for high-precision carrier frequency offset estimation.

[0045] Figure 11 This diagram illustrates the frequency convergence process obtained by fitting the motion-compensated phase using the Recursive Least Squares (RLS) method within the observation period. The horizontal axis represents the observation time, and the vertical axis represents the estimated carrier frequency. The solid blue line represents the point-by-point iterations of the RLS algorithm, and the dashed red line represents the set frequency. The curve trends show that in the initial stage (0-5 seconds), the RLS correction result deviates significantly from the set value due to high initial uncertainty in the algorithm. In the convergence stage (6-10 seconds), the estimated value decreases rapidly and gradually stabilizes, converging towards the set frequency. This indicates that RLS iterates and continuously optimizes the estimate point-by-point. In the stable stage (11-20 seconds), the estimated value almost coincides with the set frequency, with only minor fluctuations, indicating that RLS has converged to a high-precision frequency offset estimate.

[0046] Figure 12This figure shows the simulation results of carrier frequency estimation under different motion platforms and signal-to-noise ratio (SNR) conditions according to an embodiment of the present invention. This embodiment selects three representative typical motion platforms as receiver carriers for simulation testing: a combat vehicle, a civilian aircraft, and a fighter jet, represented by the blue circular curve, orange-red diamond curve, and yellow square curve in the figure, respectively. The simulation conditions are set with an SNR range of 5dB to 15dB, and the vertical axis represents the root mean square error (RMSE) of carrier frequency estimation, in Hz. As can be seen from the figure, with the gradual increase of the SNR, the RMSE of frequency estimation for all three motion platforms shows a significant decreasing trend, indicating that the method of the present invention can achieve stable and effective carrier frequency estimation under different SNR conditions. Throughout the entire simulated SNR range, even in the most intense "fighter jet" scenario, the carrier frequency estimation error of the present invention remains within a certain range. The frequency recovery rate is on the order of Hz, which fully demonstrates that the positioning-assisted phase motion compensation combined with RLS recursive estimation method adopted in this invention can greatly eliminate the influence of Doppler frequency shift and motion on frequency estimation and achieve extremely high-precision frequency recovery.

[0047] The embodiments of this invention have the following advantages: They are applicable to non-cooperative reception scenarios where the receiver is in motion. By introducing positioning assistance information for motion phase compensation, the impact of relative motion on carrier frequency estimation results is effectively reduced, overcoming the poor applicability of existing technologies in mobile situations. They achieve rapid acquisition and stable locking of non-cooperative signals, quickly determining the candidate carrier frequency range of the target signal in a short time, thus improving signal locking efficiency. They are highly practical, requiring no prior synchronization information from the transmitter. The system can not only quickly converge to the true frequency but also maintain extremely high frequency estimation stability during long-term observation. Digital signal processing is performed in the pure digital domain, greatly reducing the size and weight of the receiver.

[0048] like Figure 2 As shown, this embodiment provides a non-cooperative signal carrier frequency estimation system under motion conditions, including: The carrier frequency coarse estimation calculation module is used to obtain the non-cooperative signal of the base station to be locked, select the energy prominent frequency point in the spectrum of the non-cooperative signal, and obtain the coarse estimation value of the carrier frequency by combining the resolution of the spectrum. The carrier frequency fine estimate calculation module is used to perform mixing and downsampling processing on the non-cooperative signal based on the coarse carrier frequency estimate to obtain a downsampled intermediate frequency signal. After windowing and nonlinear transformation of the downsampled intermediate frequency signal, it is converted from the time domain to the frequency domain to obtain a single spectrum. Based on the single spectrum and using the DFT spectral peak fractional correction method, the fine estimate of the frequency of the downsampled intermediate frequency signal is obtained. The fine estimate of the frequency is used to compensate the coarse carrier frequency estimate to obtain the fine carrier frequency estimate. The carrier phase observation calculation module is used to select the observation time period of the non-cooperative signal, and perform mixing downsampling, windowing and nonlinear transformation on the non-cooperative signal within the observation time period according to the carrier frequency fine estimate, and then calculate the carrier phase observation value from the time domain to the frequency domain. The motion phase compensation module is used to obtain the transmission distance between the receiver and the base station to be locked, and to construct a motion phase compensation term based on the transmission distance and the carrier frequency fine estimate. The carrier phase observation value is compensated based on the motion phase compensation term to obtain the compensated phase sequence. The carrier frequency accurate estimation module is used to calculate the carrier frequency deviation using the recursive least squares method after the compensated phase sequence is expressed in a standard linear regression form, and to compensate the carrier frequency fine estimate value according to the carrier frequency deviation to obtain the accurate carrier frequency estimate value of the non-cooperative signal within the observation period.

[0049] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0050] This embodiment also provides a computer device, which includes a processor and a memory. The memory stores a computer program (in this embodiment, the computer program includes computational components and iterative components, capable of model calculation and model updating). The computer program includes program instructions, and the processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to realize the corresponding method flow or corresponding function. The processor of this embodiment can be used for the operation of a non-cooperative signal carrier frequency estimation method in motion.

[0051] This embodiment also provides a storage medium, specifically a computer-readable storage medium (Memory). A computer-readable storage medium is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the non-cooperative signal carrier frequency estimation method in motion state described in the above embodiment.

[0052] This embodiment also provides a computer program product, which includes a computer program that, when executed by a processor, implements the corresponding steps of the non-cooperative signal carrier frequency estimation method under motion conditions described in the above embodiment.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for estimating the carrier frequency of a non-cooperative signal under motion conditions, characterized in that, Includes the following steps: Obtain the non-cooperative signal of the base station to be locked, select the energy prominent frequency point in the spectrum of the non-cooperative signal, and obtain a coarse estimate of the carrier frequency by combining the resolution of the spectrum. The non-cooperative signal is downsampled and mixed based on the coarse estimate of the carrier frequency to obtain the downsampled intermediate frequency (IF) signal. The downsampled IF signal is then windowed and nonlinearly transformed to obtain a single spectrum from the time domain to the frequency domain. Based on the single spectrum and using the DFT spectral peak fractional correction method, a fine estimate of the frequency of the downsampled IF signal is obtained. The coarse estimate of the carrier frequency is then compensated using the fine estimate to obtain a fine estimate of the carrier frequency. The observation time period of the non-cooperative signal is selected. Based on the fine estimate of the carrier frequency, the non-cooperative signal within the observation time period is sequentially subjected to mixing downsampling, windowing processing and nonlinear transformation. The carrier phase observation value is then calculated from the time domain to the frequency domain. The transmit / receive distance between the receiver and the base station to be locked is obtained, and a motion phase compensation term is constructed based on the transmit / receive distance and a fine estimate of the carrier frequency. The carrier phase observation value is then compensated based on the motion phase compensation term to obtain the compensated phase sequence. After the compensated phase sequence is expressed in the standard linear regression form, the carrier frequency deviation is calculated using the recursive least squares method. The carrier frequency deviation is then used to compensate the fine estimate of the carrier frequency to obtain the accurate carrier frequency estimate of the non-cooperative signal within the observation period.

2. The method for estimating the carrier frequency of a non-cooperative signal under motion conditions according to claim 1, characterized in that, Selecting energy-prominent frequency points from the spectrum of the non-cooperative signal, and considering the resolution of the spectrum, includes: After applying a Blackman window to the non-cooperative signal, the non-cooperative signal is converted from the time domain to the frequency domain by a short-time Fourier transform. The spectral energy in the frequency domain is sorted, and the energy prominence frequency points with spectral energy greater than a preset prominence value are selected as signals of interest. The resolution of the spectrum is calculated based on the length of the Blackman window and the sampling rate of the non-cooperative signal; Multiplying the energy prominence frequency point by the resolution of the spectrum yields a coarse estimate of the carrier frequency.

3. The method for estimating the carrier frequency of a non-cooperative signal under motion conditions according to claim 1, characterized in that, The process of converting the downsampled intermediate frequency signal from the time domain to the frequency domain after windowing and nonlinear transformation to obtain a single spectrum includes: applying a Blackman window to the downsampled intermediate frequency signal, performing a square transformation on the windowed downsampled intermediate frequency signal, and then obtaining a single spectrum through a discrete Fourier transform.

4. The method for estimating the carrier frequency of a non-cooperative signal under motion conditions according to claim 1, characterized in that, Based on the single spectrum and using the DFT spectral peak fractional correction method, a precise frequency estimate of the downsampled intermediate frequency signal is obtained, including: Search for spectral peaks and their adjacent spectral lines in the single spectrum to obtain the corresponding discrete Fourier transform complex spectrum values; Calculate the fractional offset based on the discrete Fourier transform complex spectrum value; Based on the frequency index corresponding to the spectral peak and the fractional offset, the precise frequency estimate of the downsampled intermediate frequency signal is obtained.

5. The method for estimating the carrier frequency of a non-cooperative signal under motion conditions according to claim 1, characterized in that, Based on the fine estimate of the carrier frequency, the non-cooperative signal within the observation period is sequentially subjected to mixing downsampling, windowing, and nonlinear transformation. The carrier phase observation value is then calculated from the time domain to the frequency domain, including: Based on the fine estimate of the carrier frequency, the non-cooperative signal within the observation period is sequentially subjected to mixing downsampling, windowing, and nonlinear transformation. Then, discrete Fourier transforms are performed at the first subcarrier frequency point and the second subcarrier frequency point to obtain the first complex spectrum value and the second complex spectrum value. The first subcarrier phase is obtained by performing an arctangent operation on the imaginary and real parts of the first complex spectrum value, and the second subcarrier phase is obtained by performing an arctangent operation on the imaginary and real parts of the second complex spectrum value. The average value of the first subcarrier phase and the second subcarrier phase is taken as the carrier phase observation value.

6. The method for estimating the carrier frequency of a non-cooperative signal under motion conditions according to claim 1, characterized in that, Obtain the transmit / receive distance between the receiver and the target base station, and construct a motion phase compensation term based on the transmit / receive distance and a fine estimate of the carrier frequency, including: The receiver's position information at each observation time within the observation period is obtained, and combined with the fixed position coordinates of the base station to be locked, the geometric distance between the receiver and the base station to be locked at each observation time is calculated as the transmission distance; Using a reference time within the observation period as a benchmark, calculate the distance increment of the geometric distance at each observation time relative to the geometric distance at the reference time; Based on the distance increment and the fine estimate of the carrier frequency, a motion phase compensation term is constructed.

7. The method for estimating the carrier frequency of a non-cooperative signal under motion conditions according to claim 1, characterized in that, Based on the compensated phase sequence expressed in standard linear regression form, the carrier frequency deviation is calculated using the recursive least squares method, including: A linear observation form of the compensated phase sequence is established within the observation period, and the linear observation form is converted into a standard linear regression form; the linear observation form includes a residual frequency offset term; The standard linear regression form is recursively estimated using the recursive least squares method to obtain the parameter vector at each observation time. The residual frequency offset term in the parameter vector at the last observation time is taken as the carrier frequency offset for the current observation period.

8. A non-cooperative signal carrier frequency estimation system under motion conditions, characterized in that, include: The carrier frequency coarse estimation calculation module is used to obtain the non-cooperative signal of the base station to be locked, select the energy prominent frequency point in the spectrum of the non-cooperative signal, and obtain the coarse estimation value of the carrier frequency by combining the resolution of the spectrum. The carrier frequency fine estimate calculation module is used to perform mixing and downsampling processing on the non-cooperative signal based on the coarse carrier frequency estimate to obtain a downsampled intermediate frequency signal. After windowing and nonlinear transformation of the downsampled intermediate frequency signal, it is converted from the time domain to the frequency domain to obtain a single spectrum. Based on the single spectrum and using the DFT spectral peak fractional correction method, the fine estimate of the frequency of the downsampled intermediate frequency signal is obtained. The fine estimate of the frequency is used to compensate the coarse carrier frequency estimate to obtain the fine carrier frequency estimate. The carrier phase observation calculation module is used to select the observation time period of the non-cooperative signal, and perform mixing downsampling, windowing and nonlinear transformation on the non-cooperative signal within the observation time period according to the carrier frequency fine estimate, and then calculate the carrier phase observation value from the time domain to the frequency domain. The motion phase compensation module is used to obtain the transmission distance between the receiver and the base station to be locked, and to construct a motion phase compensation term based on the transmission distance and the carrier frequency fine estimate. The carrier phase observation value is compensated based on the motion phase compensation term to obtain the compensated phase sequence. The carrier frequency accurate estimation module is used to calculate the carrier frequency deviation using the recursive least squares method after the compensated phase sequence is expressed in a standard linear regression form, and to compensate the carrier frequency fine estimate value according to the carrier frequency deviation to obtain the accurate carrier frequency estimate value of the non-cooperative signal within the observation period.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the non-cooperative signal carrier frequency estimation method under motion conditions as described in any one of claims 1 to 7.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the non-cooperative signal carrier frequency estimation method under motion state as described in any one of claims 1 to 7.

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