A weak signal fast acquisition method and system of GNSS receiver
By dynamically adjusting the tuning parameters of the PMF-FFT algorithm by calculating multiple feature values of satellite signals, the problem of insufficient weak signal acquisition performance of GNSS receivers in high dynamic environments is solved, and fast parallel acquisition is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHAANXI QIHANG BEIDOU INFORMATION TECH CO LTD
- Filing Date
- 2026-01-05
- Publication Date
- 2026-06-19
AI Technical Summary
Existing GNSS receivers struggle to dynamically adapt to rapid changes in Doppler frequency shift and signal distortion in highly dynamic environments, leading to a decline in weak signal acquisition performance.
By calculating multiple characteristic values of the satellite signal, including the first characteristic value, the second characteristic value, and the third characteristic value, the fluctuation characteristic value is obtained, and the tuning parameters of the PMF-FFT algorithm are dynamically adjusted to adapt to Doppler frequency shift and signal distortion.
It improves the performance of GNSS receivers in rapidly acquiring weak signals in highly dynamic environments, reduces the scallop loss of the PMF-FFT algorithm, and enhances signal detection sensitivity and acquisition speed.
Smart Images
Figure CN121741776B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of rapid acquisition of weak satellite signals, and specifically to a method and system for rapid acquisition of weak signals in a GNSS receiver. Background Technology
[0002] Rapid acquisition of weak signals by GNSS receivers refers to the technical process by which GNSS receivers, in complex environments with extremely low satellite signal power, optimize algorithms and hardware architecture to quickly achieve coarse synchronization and confirmation of the satellite signal's carrier frequency and pseudocode phase, thereby establishing a communication link between the receiver and the satellite. This rapid parallel acquisition of weak signals can be achieved using the PMF-FFT algorithm.
[0003] High-speed movement of the receiver carrier can cause significant Doppler frequency shift in satellite signals. This rapid Doppler frequency drift leads to energy dispersion in weak signals, a decrease in signal-to-noise ratio, and a significant increase in acquisition difficulty. The PMF-FFT algorithm inherently suffers from scallop loss, which is typically suppressed by windowing. However, the tuning parameters of the window function are determined empirically, making it difficult to adapt to carrier frequency offsets caused by rapid changes in Doppler frequency shift in high-dynamic environments. Furthermore, it cannot cope with signal distortion caused by multipath effects in different receiving channels, resulting in decreased satellite signal acquisition performance and weakened ability to quickly acquire weak signals. Summary of the Invention
[0004] This invention provides a method and system for rapid acquisition of weak signals in a GNSS receiver, addressing the problem that the tuning parameters cannot dynamically adapt to rapid changes in Doppler frequency shift and signal distortion during rapid parallel acquisition of weak signals, resulting in insufficient weak signal acquisition capability. The specific technical solution adopted is as follows:
[0005] In a first aspect, one embodiment of the present invention provides a method for rapid acquisition of weak signals by a GNSS receiver, the method comprising the following steps:
[0006] Different GNSS receivers were used to receive satellite signals in different periods and framed to obtain the Doppler frequency shift at different acquisition times.
[0007] Any frame of satellite signal is denoted as the target satellite signal. Based on the wavelet decomposition results of the target satellite signal at different time window lengths, the first characteristic value of the target satellite signal is calculated. Based on the complexity of the target satellite signal structure at different scales, the second characteristic value of the target satellite signal is calculated. Combining the differences between all frames of satellite signals received by all GNSS receivers in the same acquisition time period corresponding to the target satellite signal, the third characteristic value of the target satellite signal is calculated. Based on the first, second, and third characteristic values of the target satellite signal, as well as the Doppler frequency shift at all acquisition times within the acquisition time period corresponding to the target satellite signal, the fluctuation characteristic value of the target satellite signal is obtained.
[0008] The adaptive values of the tuning parameters of the PMF-FFT algorithm are determined based on the fluctuation characteristics of satellite signals, thereby improving the performance of fast acquisition of weak signals.
[0009] Furthermore, the specific calculation method for the first characteristic value of the target satellite signal is as follows:
[0010] Different time window lengths for different signals are preset. Wavelet decomposition is performed on the target satellite signal under each preset time window length. Local energy is calculated and a local energy matrix is established. The maximum eigenvalue obtained by eigenvalue decomposition of the local energy matrix is used as the distribution eigenvalue of the corresponding time window length.
[0011] The positive correlation processing result of the distribution characteristic values of the target satellite signal across all preset signal time windows is recorded as the first characteristic value of the target satellite signal.
[0012] Furthermore, the specific calculation method for the second characteristic value of the target satellite signal is as follows:
[0013] Non-overlapping coarse-grained processing is performed on the target satellite signal at different preset scales. The positive correlation processing results of the differential entropy of the processing results at all preset scales are recorded as the second characteristic value of the target satellite signal.
[0014] Furthermore, the specific calculation method for the third characteristic value of the target satellite signal is as follows:
[0015] Calculate the stage characteristic value of the target satellite signal based on the first and second characteristic values of the target satellite signal;
[0016] Based on the differences in stage characteristic values between the target satellite signal and satellite signals collected by all other GNSS receivers within the same time period, the important characteristic values of the target satellite signal are calculated.
[0017] Based on the important feature values of all frames of satellite signals received by all GNSS receivers during the same acquisition time period, calculate the spatial feature values of the same acquisition time period.
[0018] The normalized value of the ratio of the stage characteristic value to the important characteristic value of the target satellite signal is denoted as the characteristic normalized value of the target satellite signal. The positive correlation between the characteristic normalized value of the target satellite signal and the spatial characteristic value of the target satellite signal during the acquisition time period is denoted as the third characteristic value of the target satellite signal.
[0019] Furthermore, the method for determining the stage characteristic values of the target satellite signal is as follows:
[0020] The sum of the first and second characteristic values of the target satellite signal is taken as the stage characteristic value of the target satellite signal.
[0021] Furthermore, the method for calculating the important characteristic values of the target satellite signal is as follows:
[0022] The mean of the absolute values of the differences between the target satellite signal and the stage characteristic values of satellite signals collected by all other GNSS receivers within the same time period is recorded as the important characteristic value of the target satellite signal.
[0023] Furthermore, the method for determining the spatial feature values is as follows:
[0024] Establish an important feature matrix for all frames of satellite signals received by all GNSS receivers corresponding to the same acquisition time period. Take the largest singular value in the singular value decomposition result of the important feature matrix as the spatial feature value of the acquisition time period of all frames of satellite signals corresponding to the important feature matrix.
[0025] Furthermore, the method for determining the fluctuation characteristic value of the target satellite signal is as follows:
[0026] The sum of the first, second, and third characteristic values of the target satellite signal is recorded as the first sum of the target satellite signal. The positive correlation between the first sum of the target satellite signal and the Doppler frequency shift of all acquisition times within the acquisition time period corresponding to the target satellite signal is recorded as the fluctuation characteristic value of the target satellite signal.
[0027] Furthermore, the specific method for determining the adaptive values of the tuning parameters of the PMF-FFT algorithm based on the fluctuation characteristic values of the satellite signal includes:
[0028] Based on the preset upper and lower limits of the parameters and the fluctuation characteristics of the target satellite signal, the adaptive tuning parameters of the target satellite signal are calculated; the adaptive tuning parameters of the satellite signal are used as the values of the tuning parameters when the PMF-FFT algorithm is used to process the satellite signal.
[0029] Secondly, embodiments of the present invention also provide a weak signal rapid acquisition system for a GNSS receiver, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.
[0030] The beneficial effects of this invention are:
[0031] This application first evaluates the severity of amplitude fluctuations in the target satellite signal based on the wavelet decomposition results at different time window lengths, obtaining the first characteristic value of the target satellite signal. Then, based on the structural complexity of the target satellite signal at different scales, it evaluates the complexity of the target satellite signal's structure at multiple scales, obtaining the second characteristic value. Next, based on the changes in the relative velocity between the satellite and receiver at all acquisition times within the acquisition time period corresponding to the target satellite signal, and combined with the differences between all frames of satellite signals received by all GNSS receivers within the same acquisition time period, it calculates the third characteristic value of the target satellite signal. Finally, based on the first and second characteristic values of the target satellite signal... The fluctuation characteristic value of the target satellite signal is obtained by combining the third characteristic value and the Doppler frequency shift of all acquisition times within the acquisition period corresponding to the target satellite signal. The larger the fluctuation characteristic value of the target satellite signal, the larger the value should be selected as the tuning parameter of the window function of the PMF-FFT algorithm to reduce the scallop loss of the PMF-FFT algorithm and enhance the ability to quickly acquire weak signals. Finally, the adaptive value of the tuning parameter of the PMF-FFT algorithm is determined according to the fluctuation characteristic value of the satellite signal to improve the fast acquisition performance of weak signals. This solves the problem that the value of the tuning parameter cannot dynamically adapt to the needs of rapid changes in Doppler frequency shift and signal distortion during the fast parallel acquisition of weak signals, resulting in insufficient acquisition capability of weak signals. Attached Figure Description
[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0033] Figure 1 This is a flowchart illustrating a method for rapid acquisition of weak signals by a GNSS receiver, provided as an embodiment of the present invention. Detailed Implementation
[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0035] Please see Figure 1 The diagram illustrates a flowchart of a method for rapid acquisition of weak signals by a GNSS receiver according to an embodiment of the present invention. The method includes the following steps:
[0036] Step S001: Use different GNSS receivers to receive satellite signals in different periods and perform frame segmentation processing to obtain the Doppler frequency shift at different acquisition times.
[0037] Different vehicle-mounted GNSS receivers are used to receive satellite signals from low-Earth orbit satellites.
[0038] Each GNSS receiver exchanges characteristic data through wireless communication networks such as V2X, ad hoc networks, or data centers.
[0039] In this embodiment, the satellite signal is an L1 signal with a frequency of 1575.42MHz. The carrier modulation method is BPSK binary phase shift keying, and spread spectrum modulation is performed using CDMA code division multiple access technology. The GNSS receiver has a sampling frequency of 50KHz and a sampling period of 10min. The signal is filtered, amplified, and converted from digital to analog at the receiver's radio frequency front end, and the digital signal is output using the baseband processing module.
[0040] The Doppler shift will vary at different acquisition times. The satellite signal of the same period is processed by frame segmentation to obtain the satellite signal of each frame within the period.
[0041] In this embodiment, the frame length for framing is set to 200ms, the frame shift is half the frame length, and Hamming windows are used to window each frame of satellite signal. Both framing and windowing are well-known technologies and will not be described in detail here.
[0042] The satellite communication system is used to obtain the motion speed of the low-Earth orbit satellite and the wave speed of the satellite signal. The speed of the vehicle carrying the GNSS receiver is obtained through a speedometer. Based on the motion speed of the low-Earth orbit satellite, the wave speed of the satellite signal, and the speed of the vehicle carrying the GNSS receiver, the Doppler frequency shift at each acquisition moment is calculated based on the inertial navigation system or ephemeris data.
[0043] This completes the acquisition of each frame of satellite signal within the acquisition period and the Doppler shift at each acquisition moment.
[0044] Step S002: Denote any frame of satellite signal as the target satellite signal. Calculate the first characteristic value of the target satellite signal based on the wavelet decomposition results of the target satellite signal at different time window lengths. Calculate the second characteristic value of the target satellite signal based on the complexity of its structure at different scales. Combine the differences between all frames of satellite signals received by all GNSS receivers within the same acquisition time period corresponding to the target satellite signal to calculate the third characteristic value of the target satellite signal. Obtain the fluctuation characteristic value of the target satellite signal based on the first, second, and third characteristic values of the target satellite signal, as well as the Doppler frequency shift at all acquisition times within the acquisition time period corresponding to the target satellite signal.
[0045] Satellite signals experience attenuation and multipath effects when passing through obstacles during propagation. This results in varying degrees of energy attenuation when the same satellite signal reaches different GNSS receivers. The more severe the energy attenuation, the greater the signal amplitude fluctuation and the higher the probability of weak signals. Furthermore, the high-speed movement of vehicle-mounted GNSS receivers causes significant Doppler shifts in satellite signals. This rapid drift further exacerbates the dispersion of weak signal energy, leading to a decrease in the signal-to-noise ratio and significantly increasing the difficulty of acquisition.
[0046] While the PMF-FFT algorithm can achieve rapid parallel acquisition of weak signals, it suffers from inherent scallop loss, which reduces the accuracy of Doppler frequency estimation and consequently affects receiver sensitivity and prolongs acquisition time. Windowing can be used to suppress scallop loss. However, the tuning parameters of the window function are determined empirically, making it difficult to adapt to carrier frequency shifts caused by rapid changes in Doppler frequency shift in high-dynamic environments. Furthermore, it cannot cope with signal distortion caused by multipath effects in different receiving channels, leading to a decline in satellite signal acquisition performance and weakening the ability to rapidly acquire weak signals.
[0047] It should be noted that the probability of a weak signal is related to the tuning parameters of the window function: the higher the probability of a weak signal, the larger the value of the window function's tuning parameters should be to effectively suppress the scallop loss of the PMF-FFT algorithm and improve the sensitivity and acquisition speed of weak signal detection. From the perspective of frequency domain characteristics, the greater the difference in satellite signal amplitude, the more difficult it is for frequency domain energy to concentrate on a few frequency points, and the more significant the dispersion of satellite signal distribution. In particular, even if the discrete frequency points are far from the energy concentration frequency points, this distribution characteristic may still be a sign of the presence of a weak signal.
[0048] Let any frame of satellite signal be denoted as the target satellite signal. Different preset time window lengths are defined. Under each preset time window length, the CWT continuous wavelet decomposition algorithm is used to perform 128-level wavelet decomposition on the target satellite signal, obtaining 128 detail components. The local energy of each detail component under each preset time window length is then calculated for each time window. Let any preset time window length be denoted as the target time window length. Based on the local energy of all time windows of all detail components under the target time window length, a local energy matrix for the target time window length is established. The local energy matrix contains the first... line, number The value of column is the first The first detail component The local energy matrix is subjected to SVD eigenvalue decomposition to obtain three eigenvalues. The largest eigenvalue is taken as the distribution eigenvalue of the target time window length. The same method can be used to obtain the distribution eigenvalue of the target satellite signal across all preset signal time window lengths. The positive correlation result of the distribution eigenvalues of the target satellite signal across all preset signal time window lengths is recorded as the first eigenvalue of the target satellite signal.
[0049] It is understood that positive correlation processing is applied to the distribution characteristic values of the target satellite signal across all preset signal time windows, ensuring a positive correlation between the distribution characteristic values of the target satellite signal across all preset signal time windows and the first characteristic value of the target satellite signal. It is understood that the positive correlation in this application refers to the relationship between the independent variable and the dependent variable. The independent variable is the distribution characteristic value of the target satellite signal across all preset signal time windows, and the dependent variable is the first characteristic value of the target satellite signal. The positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and can be an additive relationship, a multiplicative relationship, etc.
[0050] Preferably, as an embodiment of this application, the average value of the distribution characteristic value of the target satellite signal over all preset time windows is recorded as the first characteristic value of the target satellite signal.
[0051] In this embodiment, the preset time window lengths for different signals are 8ms, 12ms, 16ms, and 20ms. The CWT continuous wavelet decomposition algorithm is used to obtain detail components and calculate the local energy of each time window of the detail components, and to perform eigenvalue decomposition on the matrix. These are well-known techniques and will not be described in detail here.
[0052] The larger the first characteristic value of the target satellite signal, the more violent the amplitude fluctuation of the target satellite signal.
[0053] Because satellite signals are affected by multipath effects differently at different times, satellite signals in different frames will exhibit varying degrees of fluctuation. A multi-scale differential entropy method is employed to analyze the multi-scale complexity of the target satellite signal.
[0054] The target satellite signal is subjected to non-overlapping coarse-grained processing to obtain coarse-grained sequences at different preset scales. The positive correlation processing results of the differential entropy of all coarse-grained sequences at preset scales are recorded as the second feature value of the target satellite signal.
[0055] Preferably, as an embodiment of this application, the mean of the differential entropy of all coarse-grained sequences at a preset scale is denoted as the second characteristic value of the target satellite signal.
[0056] In this embodiment, the actual values of the preset scale are 1, 2, and 3; the non-overlapping coarse-graining processing and the calculation of differential entropy are all well-known techniques.
[0057] The larger the second characteristic value of the target satellite signal, the more complex the structure of the target satellite signal at multiple scales, the higher the randomness, and the more violent the amplitude fluctuation.
[0058] Different multipath effects can lead to differences in the fluctuation of satellite signals in different frames. Specifically, satellite signals acquired by different GNSS receivers in the same frame of the same period will be affected by different local multipath and obstruction conditions of different GNSS receivers, resulting in differences in the fluctuation characteristics of satellite signals.
[0059] Therefore, the similarity of different frames of satellite signals collected by different GNSS receivers within the same time period can more intuitively reflect the essential characteristics of satellite signals within the corresponding time period, while the difference in signal fluctuations of different frames of satellite signals collected by different GNSS receivers within the same time period can more intuitively reflect the degree to which satellite signals within the corresponding time period are affected by multipath effects.
[0060] The sum of the first and second characteristic values of the target satellite signal is taken as the stage characteristic value of the target satellite signal. The mean of the absolute values of the differences between the stage characteristic values of the target satellite signal and the satellite signals collected by all other GNSS receivers in the same time period is recorded as the important characteristic value of the target satellite signal.
[0061] The same method can be used to obtain the stage characteristic values and important characteristic values of each frame of satellite signal received by each GNSS receiver.
[0062] Based on the important feature values of all frames of satellite signals received by all GNSS receivers within the same acquisition time period, an important feature matrix is established. The important feature matrix contains the first... line, number The value of column is the first The GNSS receiver received the first Important feature values of frame satellite signals. The singular value decomposition algorithm is used to obtain the three singular values of the important feature matrix. The largest singular value is taken as the spatial feature value of all frame satellite signals acquired within the corresponding time period.
[0063] Spatial feature values are used to evaluate the relative fluctuation of satellite signals in different frames within the corresponding acquisition time period under spatiotemporal relationships.
[0064] The third characteristic value of the target satellite signal is calculated based on the stage characteristic value and important characteristic value of the target satellite signal, as well as the spatial characteristic value of the target satellite signal acquisition time period.
[0065] The normalized value of the ratio of the stage characteristic value to the important characteristic value of the target satellite signal is denoted as the characteristic normalized value of the target satellite signal. The positive correlation between the characteristic normalized value of the target satellite signal and the spatial characteristic value of the target satellite signal during the acquisition time period is denoted as the third characteristic value of the target satellite signal.
[0066] In the process of calculating the ratio, to avoid the denominator being zero, a preset value needs to be added to the denominator. In this embodiment, the preset value is 0.01. In this embodiment, the maximum-minimum normalization method is used to calculate the normalized value. The maximum-minimum normalization method is a well-known technique and will not be described in detail here. As other implementation methods, implementers can use other methods of the prior art, such as the tanh function.
[0067] It is understood that a positive correlation is applied to the normalized feature value of the target satellite signal and the spatial feature value of the target satellite signal during the acquisition time period. This ensures that the normalized feature value of the target satellite signal and the spatial feature value of the target satellite signal during the acquisition time period are positively correlated with the third feature value of the target satellite signal. It is understood that the positive correlation in this application refers to the relationship between the independent and dependent variables. The independent variables are the normalized feature value of the target satellite signal and the spatial feature value of the target satellite signal during the acquisition time period, and the dependent variable is the third feature value of the target satellite signal. The positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and can be an additive or multiplicative relationship.
[0068] Preferably, as an embodiment of this application, the product of the normalized feature value of the target satellite signal and the spatial feature value of the target satellite signal during the acquisition time period is recorded as the third feature value of the target satellite signal.
[0069] Within the acquisition time period corresponding to the target satellite signal, the greater the change in the relative velocity between the satellite and the receiver at all acquisition moments, the larger the third characteristic value of the target satellite signal.
[0070] The first, second, and third characteristic values of the target satellite signal, along with the positive correlation processing results of the Doppler frequency shift at all acquisition times within the acquisition period corresponding to the target satellite signal, are denoted as the fluctuation characteristic values of the target satellite signal.
[0071] Preferably, as an embodiment of this application, the sum of the first characteristic value, the second characteristic value, and the third characteristic value of the target satellite signal is recorded as the first sum of the target satellite signal, the absolute value of the mean of the Doppler frequency shift at all acquisition times within the acquisition time period corresponding to the target satellite signal is recorded as the first mean of the target satellite signal, and the normalized value of the product of the first sum of the target satellite signal and the first mean is recorded as the fluctuation characteristic value of the target satellite signal.
[0072] In this embodiment, the sigmoid function is used to calculate the normalized value. The sigmoid function is a well-known technique and will not be described in detail here. As other implementation methods, implementers can use other methods of the prior art, such as the tanh function.
[0073] When the target satellite signal is more significantly affected by multipath effects, has a more complex structure at different preset time scales, and exhibits smaller fluctuations between the target satellite signal and other satellite signals at different spatial locations, and when the relative velocity between the satellite and the receiver changes more significantly at all acquisition times within the acquisition period corresponding to the target satellite signal, the target satellite signal is more affected by high-dynamic scenes. In this case, the fluctuation characteristic value of the target satellite signal is larger, and a larger value should be selected as the tuning parameter of the window function of the PMF-FFT algorithm to reduce the scallop loss of the PMF-FFT algorithm and enhance the ability to quickly capture weak signals.
[0074] At this point, the fluctuation characteristic values of the target satellite signal are obtained.
[0075] Step S003: Determine the adaptive values of the tuning parameters of the PMF-FFT algorithm based on the fluctuation characteristics of the satellite signal to improve the fast acquisition performance of weak signals.
[0076] The adaptive tuning parameters of the target satellite signal are calculated based on the fluctuation characteristics of the target satellite signal.
[0077] Specifically, the preset upper and lower limits of the parameters are the upper and lower limits of the adaptive tuning parameters, respectively. In this embodiment, the upper and lower limits of the parameters are 10 and 4, respectively.
[0078] Preferably, as an embodiment of this application, the product of the difference between the preset upper limit value and the lower limit value of the parameter and the fluctuation characteristic value of the target satellite signal is recorded as the first product of the target satellite signal, and the rounded value of the sum of the first product of the target satellite signal and the preset lower limit value of the parameter is recorded as the adaptive tuning parameter of the target satellite signal.
[0079] The same method can be used to obtain the adaptive tuning parameters for each frame of satellite signal received by each GNSS receiver.
[0080] By using the adaptive tuning parameters of the satellite signal as the values of the tuning parameters of the satellite signal, the PMF-FFT algorithm is used to process all frames of satellite signals in all periods received by all GNSS receivers, effectively extracting satellite signals submerged in noise, reducing the scallop loss of the PMF-FFT algorithm, and enhancing the ability to quickly capture weak signals.
[0081] In this embodiment, the number of filters in the PMF-FFT algorithm is set to 128; the PMF-FFT algorithm is a well-known technology and will not be described in detail here.
[0082] This enables the rapid acquisition of weak signals by the GNSS receiver.
[0083] Based on the same inventive concept as the above method, this embodiment of the invention also provides a weak signal rapid acquisition system for a GNSS receiver, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for rapid weak signal acquisition of a GNSS receiver.
[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A weak signal fast acquisition method for a GNSS receiver, characterized in that, The method includes the following steps: Different GNSS receivers were used to receive satellite signals in different periods and framed to obtain the Doppler frequency shift at different acquisition times. Any frame of satellite signal is denoted as the target satellite signal. Based on the wavelet decomposition results of the target satellite signal at different time window lengths, the first characteristic value of the target satellite signal is calculated. Based on the complexity of the target satellite signal structure at different scales, the second characteristic value of the target satellite signal is calculated. Combining the differences between all frames of satellite signals received by all GNSS receivers in the same acquisition time period corresponding to the target satellite signal, the third characteristic value of the target satellite signal is calculated. Based on the first, second, and third characteristic values of the target satellite signal, as well as the Doppler frequency shift at all acquisition times within the acquisition time period corresponding to the target satellite signal, the fluctuation characteristic value of the target satellite signal is obtained. The adaptive values of the tuning parameters of the PMF-FFT algorithm are determined based on the fluctuation characteristics of satellite signals, thereby improving the performance of fast acquisition of weak signals.
2. The method of claim 1, wherein, The specific method for calculating the first characteristic value of the target satellite signal is as follows: Different time window lengths for different signals are preset. Wavelet decomposition is performed on the target satellite signal under each preset time window length. Local energy is calculated and a local energy matrix is established. The maximum eigenvalue obtained by eigenvalue decomposition of the local energy matrix is used as the distribution eigenvalue of the corresponding time window length. The positive correlation processing result of the distribution characteristic values of the target satellite signal across all preset signal time windows is recorded as the first characteristic value of the target satellite signal.
3. The method of claim 1, wherein, The specific method for calculating the second characteristic value of the target satellite signal is as follows: Non-overlapping coarse-grained processing is performed on the target satellite signal at different preset scales. The positive correlation processing results of the differential entropy of the processing results at all preset scales are recorded as the second characteristic value of the target satellite signal.
4. The method of claim 1, wherein, The specific calculation method for the third characteristic value of the target satellite signal is as follows: Calculate the stage characteristic value of the target satellite signal based on the first and second characteristic values of the target satellite signal; Based on the differences in stage characteristic values between the target satellite signal and satellite signals acquired by all other GNSS receivers within the same time period, the important characteristic values of the target satellite signal are calculated. Based on the important feature values of all frames of satellite signals received by all GNSS receivers during the same acquisition time period, calculate the spatial feature values of the same acquisition time period. The normalized value of the ratio of the stage characteristic value to the important characteristic value of the target satellite signal is denoted as the characteristic normalized value of the target satellite signal. The positive correlation between the characteristic normalized value of the target satellite signal and the spatial characteristic value of the target satellite signal during the acquisition time period is denoted as the third characteristic value of the target satellite signal.
5. The method of claim 4, wherein, The method for determining the stage characteristic values of the target satellite signal is as follows: The sum of the first and second characteristic values of the target satellite signal is taken as the stage characteristic value of the target satellite signal.
6. The method of claim 4, wherein, The method for calculating the important characteristic values of the target satellite signal is as follows: The mean of the absolute values of the differences between the target satellite signal and the stage characteristic values of satellite signals collected by all other GNSS receivers within the same time period is recorded as the important characteristic value of the target satellite signal.
7. The method of claim 4, wherein, The method for determining the spatial feature values is as follows: Establish an important feature matrix for all frames of satellite signals received by all GNSS receivers corresponding to the same acquisition time period. Take the largest singular value in the singular value decomposition result of the important feature matrix as the spatial feature value of the acquisition time period of all frames of satellite signals corresponding to the important feature matrix.
8. The method of claim 1, wherein, The method for determining the fluctuation characteristic value of the target satellite signal is as follows: The sum of the first, second, and third characteristic values of the target satellite signal is recorded as the first sum of the target satellite signal. The positive correlation between the first sum of the target satellite signal and the Doppler frequency shift of all acquisition times within the acquisition time period corresponding to the target satellite signal is recorded as the fluctuation characteristic value of the target satellite signal.
9. A method for rapid acquisition of weak signals in a GNSS receiver according to claim 1, characterized in that, The method for adaptively determining the tuning parameters of the PMF-FFT algorithm based on the fluctuation characteristics of satellite signals includes the following specific methods: Based on the preset upper and lower limits of the parameters and the fluctuation characteristics of the target satellite signal, the adaptive tuning parameters of the target satellite signal are calculated; the adaptive tuning parameters of the satellite signal are used as the values of the tuning parameters when the PMF-FFT algorithm is used to process the satellite signal.
10. A system for rapid acquisition of weak signals for a GNSS receiver, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as claimed in any one of claims 1-9.