Multi-path parallel high-dynamic wireless receiving system and method

Abstract

The invention belongs to the technical field of multi-channel parallel signal wireless transmission, and particularly relates to a multi-channel parallel high-dynamic wireless receiving system and a multi-channel parallel high-dynamic wireless receiving method. According to the invention, the wireless signal is segmented to form multiple paths of parallel signals, and each path of parallel signal is filtered by using the adaptive filter, so that interference noise in the parallel signals is effectively removed, the signal quality is improved, and meanwhile, the signal quality is improved. The parameters and the weight coefficient of the adaptive filter are dynamically adjusted according to the risk score of the error signal, fine adjustment of the adaptive filter is achieved, mutual feedback can be achieved in the coarse adjustment stage and the fine adjustment stage in the adjustment process of the weight coefficient, it is ensured that the weight coefficient can be rapidly and accurately converged to the optimal solution, and the accuracy of the adaptive filter is improved. In addition, a variable step size strategy is introduced, the step size factor is dynamically adjusted according to the change of the error signal, the adjustment process of the weight coefficient is further optimized, and oscillation and errors in the adjustment process are reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of multi-channel parallel signal wireless transmission, and in particular relates to a high-dynamic wireless receiving system and method under multi-channel parallel conditions. Background Art

[0002] With the rapid development of wireless communication technology, especially in high-dynamic environments, such as satellite communications, high-speed moving vehicle communications and other scenarios, higher requirements are placed on the performance and stability of wireless receiving systems. However, with the increase in signal transmission rates, traditional wireless receiving methods have gradually exposed problems such as high processing delay and large signal distortion. Therefore, how to achieve efficient and stable wireless signal reception in a high-dynamic environment has become a technical problem that needs to be solved urgently.

[0003] In the prior art, multiple independent receiving channels are usually used to process wireless signals. This approach not only increases the complexity of the system, but also makes it difficult to effectively eliminate interference and noise between channels when processing parallel signals, thereby affecting the reception quality and stability of the signal. Based on this, the present invention proposes a high-dynamic wireless receiving method under multi-path parallelism, aiming to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a high-dynamic wireless receiving system and method under multi-path parallelism, which can realize efficient and stable wireless signal reception in a high-dynamic environment and improve the reception quality and stability of the signal.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A high dynamic wireless receiving method under multi-path parallelism, comprising:

[0007] The wireless signal is segmented to form multiple parallel signals, wherein each parallel signal corresponds to a communication channel;

[0008] According to a preset expected response, counting the error signal output in the wireless signal, and using the error signal to adjust the parameters of the adaptive filter;

[0009] Each parallel signal is filtered by an adaptive filter to remove interference noise in the parallel signal and output as a signal to be processed;

[0010] Adjusting the weight coefficient of the adaptive filter according to the error signal and the input signal, wherein the weight coefficient adjustment includes a coarse adjustment stage and a fine adjustment stage;

[0011] In the coarse adjustment stage, the weight coefficients of the adaptive filter are preliminarily adjusted to be close to convergence;

[0012] In the fine-tuning stage, the weight coefficient after the preliminary adjustment is fine-tuned until the weight coefficient converges to the optimal solution;

[0013] The signals to be processed after weight processing are combined to restore the original wireless signals and output them to the wireless receiving device.

[0014] In a preferred solution, the step of segmenting the wireless signal to form multiple parallel signals includes:

[0015] Acquire an original wireless signal, and a signal strength and frequency of the original wireless signal;

[0016] Obtaining the required number of segmentations of the original wireless signal, and determining the bandwidth and modulation mode of each communication channel according to the signal strength and frequency;

[0017] According to the required number of segmentations, the original wireless signal is segmented using Fourier transform, the signal is converted into the frequency domain, multiple parallel signals are obtained, and each parallel signal is then allocated to the corresponding communication channel.

[0018] In a preferred solution, the step of counting the error signal output from the wireless signal according to the preset expected response includes:

[0019] Obtaining a preset expected response signal;

[0020] Calculating the difference between the wireless signal and the expected response signal to obtain an error signal, and calculating the occurrence frequency and error amplitude of the error signal;

[0021] Determining a risk score of the error signal according to the frequency of occurrence and the error amplitude of the error signal, and determining whether the error signal is added to the parameter adjustment process of the adaptive filter according to the risk score;

[0022] The error signal is used to adjust the parameters of the adaptive filter to reduce the error between the signal to be processed and the expected response signal.

[0023] In a preferred embodiment, the step of determining a risk score of the error signal based on the occurrence frequency and error amplitude of the error signal, and determining whether to add the error signal to the parameter adjustment process of the adaptive filter according to the risk score includes:

[0024] Obtaining the occurrence frequency and error amplitude of the error signal, and recording them as a first evaluation parameter and a second evaluation parameter respectively;

[0025] Obtaining an evaluation function, and inputting the first evaluation parameter and the second evaluation parameter into the evaluation function together, and recording an output result of the evaluation function as a risk score;

[0026] Obtaining a preset risk threshold, and comparing the risk threshold with the risk score;

[0027] When the risk score is greater than or equal to a risk threshold, adding the error signal to a parameter adjustment process of an adaptive filter;

[0028] When the risk score is less than the risk threshold, the error signal is ignored and no adjustment is made to the adaptive filter parameters.

[0029] In a preferred solution, the step of filtering each parallel signal by an adaptive filter includes:

[0030] Obtaining known interference noise characteristics and an error signal added to a parameter adjustment process of an adaptive filter;

[0031] Setting initial filtering parameters of the adaptive filter according to the interference noise characteristics and an error signal added to the parameter adjustment process of the adaptive filter;

[0032] The parallel signals are input into an adaptive filter for processing to remove interference noise in the parallel signals.

[0033] In a preferred embodiment, the step of adjusting the weight coefficient of the adaptive filter according to the error signal and the input signal comprises:

[0034] Set the initial weight coefficient and calculate the gradient vector based on the error signal and the input signal;

[0035] Multiplying the gradient vector by a preset learning rate to obtain an adjustment amount of the weight coefficient;

[0036] Adding the adjustment amount of the weight coefficient to the initial weight coefficient to obtain an updated weight coefficient, until the weight coefficient initially converges to a solution close to the optimal solution, completing the coarse adjustment stage;

[0037] The weight coefficient outputted in the coarse jump phase is recorded as the fine-tuning initial weight in the fine-tuning phase;

[0038] Performing adaptive evolution processing on the fine-tuning initial weights, searching for a global optimal solution by iteratively updating the covariance matrix of the fine-tuning initial weights;

[0039] In each iteration, a set of candidate weight coefficients is generated according to the current weight coefficient, and the fitness value of each candidate weight coefficient is calculated;

[0040] The candidate weight coefficient with the best fitness value is selected as the current weight coefficient for the next iteration, and the iterative process is repeated until the weight coefficient converges to the global optimal solution, completing the fine-tuning stage.

[0041] In a preferred solution, when the weight coefficient of the adaptive filter is adjusted, a variable step size strategy for dynamically adjusting the step size factor is also included, and the specific process is as follows:

[0042] Obtain the instantaneous value and historical value of the error signal, as well as the gradient information of the current weight coefficient;

[0043] Calculating the rate of change of the error signal according to the instantaneous value and the historical value of the error signal;

[0044] Dynamically matching the corresponding step size factor according to the rate of change of the error signal and the gradient information of the current weight coefficient;

[0045] The step factor increases when the error signal changes greatly, and decreases when the error signal changes less.

[0046] In a preferred solution, the step of performing signal merging processing on the signal to be processed after weight processing to restore the original wireless signal includes:

[0047] Perform inverse Fourier transform on each signal to be processed after weight processing, convert the frequency domain signal back to the time domain, and obtain parallel signals in the time domain;

[0048] Perform time synchronization processing on the parallel signals in each time domain to determine the temporal consistency of each parallel signal;

[0049] By using the signal superposition method, the time-synchronized parallel signals are merged to obtain a merged composite signal;

[0050] The combined composite signal is decoded and the original wireless signal is restored according to the preset coding rules.

[0051] The present invention also provides a high-dynamic wireless receiving system under multi-path parallel operation, using the above-mentioned high-dynamic wireless receiving method under multi-path parallel operation, comprising:

[0052] A signal segmentation module, wherein the signal segmentation module is used to segment the wireless signal to form multiple parallel signals, wherein each parallel signal corresponds to a communication channel;

[0053] A parameter adjustment module, the parameter adjustment module is used to count the error signal output from the wireless signal according to a preset expected response, and use the error signal to adjust the parameters of the adaptive filter;

[0054] A filtering module, wherein the filtering module is used to filter each parallel signal through an adaptive filter, remove interference noise in the parallel signal, and output it as a signal to be processed;

[0055] A weight adjustment module, the weight adjustment module is used to adjust the weight coefficient of the adaptive filter according to the error signal and the input signal, wherein the weight coefficient adjustment includes a coarse adjustment stage and a fine adjustment stage;

[0056] In the coarse adjustment stage, the weight coefficients of the adaptive filter are preliminarily adjusted to be close to convergence;

[0057] In the fine-tuning stage, the weight coefficient after the preliminary adjustment is fine-tuned until the weight coefficient converges to the optimal solution;

[0058] The signal merging module is used to perform signal merging processing on the signals to be processed after weight processing, restore the original wireless signals, and output them to the wireless receiving device.

[0059] And, an electronic device, the electronic device comprising:

[0060] at least one processor;

[0061] and a memory communicatively coupled to the at least one processor;

[0062] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned high-dynamic wireless receiving method under multi-path parallelism.

[0063] The technical effects achieved by the present invention are:

[0064] The present invention forms multiple parallel signals by segmenting the wireless signal, and uses an adaptive filter to filter each parallel signal, thereby effectively removing interference noise in the parallel signal and improving signal quality. At the same time, the present invention also realizes fine adjustment of the adaptive filter by dynamically adjusting the parameters and weight coefficient of the adaptive filter according to the risk score of the error signal, further improving the accuracy and stability of signal processing. In addition, during the adjustment of the weight coefficient, the coarse adjustment stage and the fine adjustment stage can provide feedback to each other to ensure that the weight coefficient can converge to the optimal solution quickly and accurately, thereby improving the efficiency and accuracy of signal processing, and significantly improving the performance of the wireless receiving system in complex environments. In addition, a variable step size strategy is introduced to dynamically adjust the step size factor according to the change of the error signal, thereby further optimizing the adjustment process of the weight coefficient, reducing the oscillation and error in the adjustment process, and further improving the stability and performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic flow chart of the method of the present invention;

[0066] Figure 2 It is a schematic diagram of the system module of the present invention;

[0067] Figure 3 It is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0069] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0070] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0071] See also Figure 1 As shown, the present invention provides a high dynamic wireless receiving method under multi-path parallel, comprising:

[0072] S1. Segment the wireless signal to form multiple parallel signals, where each parallel signal corresponds to a communication channel;

[0073] In step S1, when processing a multi-channel parallel signal, it is first necessary to split the input wireless signal into multiple parallel signals, and each parallel signal corresponds to an independent communication channel to ensure the parallelism and efficiency of signal processing. The step of splitting the wireless signal to form multiple parallel signals includes:

[0074] Obtaining the original wireless signal, as well as the signal strength and frequency of the original wireless signal;

[0075] Obtain the required number of segmentations of the original wireless signal, and determine the bandwidth and modulation mode of each communication channel according to the signal strength and frequency;

[0076] According to the required number of segmentations, the original wireless signal is segmented using Fourier transform, the signal is converted into the frequency domain, multiple parallel signals are obtained, and each parallel signal is then allocated to the corresponding communication channel;

[0077] Specifically, when the wireless signal is segmented, the basic information of the original wireless signal is first comprehensively acquired, which covers the data of the original wireless signal itself, as well as key parameters such as signal strength and frequency related thereto. Secondly, the specific number of segmentations of the original wireless signal needs to be acquired. On this basis, the bandwidth size and appropriate modulation method to be allocated to each communication channel are determined in combination with the acquired signal strength and frequency information to ensure the stability and efficiency of signal transmission. Finally, Fourier transform is used for processing according to the pre-set number of required segmentations to realize the segmentation operation of the original wireless signal, thereby converting the original wireless signal from the time domain to the frequency domain, and then obtaining multiple parallel signals. Each parallel signal is then allocated to the corresponding communication channel one by one, so as to realize the parallel transmission of multiple signals.

[0078] S2. According to a preset expected response, counting the error signal output from the wireless signal, and using the error signal to adjust the parameters of the adaptive filter;

[0079] In step S2, after the wireless signal segmentation is completed, error statistics are performed on each parallel signal according to a preset expected response. Specifically, the error signal is extracted by comparing the difference between the actual output signal and the expected response, and the error signal is used to dynamically adjust various parameters of the adaptive filter to optimize the filtering effect. The step of counting the error signal output in the wireless signal according to the preset expected response includes:

[0080] Obtaining a preset expected response signal;

[0081] Calculate the difference between the wireless signal and the expected response signal to obtain an error signal, and count the occurrence frequency and error amplitude of the error signal;

[0082] Determining a risk score of the error signal based on the frequency of occurrence and the error amplitude of the error signal, and determining whether the error signal should be added to the parameter adjustment process of the adaptive filter based on the risk score;

[0083] The error signal is used to adjust the parameters of the adaptive filter to reduce the error between the signal to be processed and the expected response signal;

[0084] Specifically, when outputting an error signal, it is first necessary to introduce a preset expected response signal, and then perform an accurate difference calculation between the wireless signal and the acquired expected response signal (X(n)=Q(n)-C(n), where X(n) represents the error signal, Q(n) represents the expected response signal, and C(n) represents the signal output by the adaptive filter). In this way, the error signal can be obtained, and then the frequency of occurrence of the error signal and the specific amplitude of the error are statistically analyzed. This process is based on the wireless signal that has been sent and received, so as to ensure the accuracy and comprehensiveness of the error signal analysis. On this basis, the risk score of the error signal can be determined according to the frequency of occurrence of the error signal and the error amplitude, and whether the error signal needs to be included in the parameter adjustment process of the adaptive filter can be determined according to the risk score. Finally, the parameters of the adaptive filter are adjusted accordingly using the screened error signal, so as to achieve the purpose of reducing the error between the wireless signal and the expected response signal, thereby improving the accuracy and stability of wireless signal processing.

[0085] Secondly, the step of determining the risk score of the error signal according to the occurrence frequency and the error amplitude of the error signal, and determining whether the error signal is added to the parameter adjustment process of the adaptive filter according to the risk score includes:

[0086] Obtaining the occurrence frequency and error amplitude of the error signal, and recording them as a first evaluation parameter and a second evaluation parameter respectively;

[0087] Obtaining an evaluation function, and inputting the first evaluation parameter and the second evaluation parameter into the evaluation function, and recording an output result of the evaluation function as a risk score;

[0088] Obtain a preset risk threshold and compare the risk threshold with the risk score;

[0089] When the risk score is greater than or equal to the risk threshold, the error signal is added to the parameter adjustment process of the adaptive filter;

[0090] When the risk score is less than the risk threshold, the error signal is ignored and no adjustment is made to the adaptive filter parameters;

[0091] Specifically, when deciding whether to include the error signal in the parameter adjustment process of the adaptive filter according to the risk score, it is first necessary to obtain the occurrence frequency and error amplitude of the error signal, and record the occurrence frequency of the error signal as the first evaluation parameter, and record the error amplitude of the error signal as the second evaluation parameter. The occurrence frequency of the error signal reflects the frequency of occurrence of the error signal, and the error amplitude reflects the strength of the error signal. Then, a predefined evaluation function is introduced, and the first evaluation parameter and the second evaluation parameter are input into the evaluation function together (f p =αP1+βP2, where fp represents the risk score, α and β represent the weight coefficients of the first evaluation parameter and the second evaluation parameter respectively, and β is greater than α, P1 and P2 represent the first evaluation parameter and the second evaluation parameter respectively), the evaluation function calculates the corresponding risk score according to the comprehensive influence of the first evaluation parameter and the second evaluation parameter, and then introduces the preset risk threshold. The risk threshold is a critical value set according to system requirements and actual application scenarios. The calculated risk score is compared with the preset risk threshold to determine whether the risk level of the error signal has reached the level that requires attention. When the risk score is greater than or equal to the risk threshold, it indicates that the error signal has a higher risk level and needs to be processed. At this time, the error signal is added to the parameter adjustment process of the adaptive filter, and the filter parameters are adjusted to reduce or eliminate the influence of the error signal. On the contrary, when the risk score is less than the risk threshold, it indicates that the risk level of the error signal is low, and the impact on the quality of the wireless signal is small and can be ignored. In this case, the error signal is not processed, and the adaptive filter parameters are not adjusted to avoid unnecessary calculation and resource consumption.

[0092] S3, filtering each parallel signal through an adaptive filter to remove interference noise in the parallel signal, and outputting it as a signal to be processed;

[0093] In step S3, after the parallel signal is output, each parallel signal is filtered by an adaptive filter according to the known error signal and the known noise interference, so as to remove various interference noises in the parallel signal and output a relatively pure signal to be processed, so as to improve the quality and efficiency of wireless signal transmission, wherein the step of filtering each parallel signal by an adaptive filter includes:

[0094] Obtaining known interference noise characteristics and an error signal added to a parameter adjustment process of an adaptive filter;

[0095] Setting initial filter parameters of the adaptive filter according to the interference noise characteristics and the error signal added to the parameter adjustment process of the adaptive filter;

[0096] The parallel signals are input into an adaptive filter for processing to remove interference noise in the parallel signals;

[0097] Specifically, when filtering parallel signals through an adaptive filter, first, known interference noise characteristics are collected. The known interference noise is usually obtained through preliminary tests or data analysis. At the same time, it is also necessary to obtain an error signal added to the parameter adjustment process of the adaptive filter. The error signal reflects the gap between the filtering effect and the ideal state. Then, the initial filtering parameters of the adaptive filter can be set according to the obtained interference noise characteristics and the error signal added to the parameter adjustment process of the adaptive filter. Finally, the parallel signal to be processed is input into the adaptive filter with the initial parameters set for processing. Through the dynamic adjustment and optimization of the adaptive filter, the interference noise in the parallel signal is gradually removed, thereby improving the signal quality and transmission efficiency.

[0098] S4, adjusting the weight coefficient of the adaptive filter according to the error signal and the input signal, wherein the weight coefficient adjustment includes a coarse adjustment stage and a fine adjustment stage;

[0099] In the coarse adjustment stage, the weight coefficients of the adaptive filter are initially adjusted to be close to convergence;

[0100] In the fine-tuning stage, the weight coefficients after the initial adjustment are fine-tuned until the weight coefficients converge to the optimal solution;

[0101] In step S4, during the filtering process of the wireless signal executed by the adaptive filter, the weight coefficient of the adaptive filter is finely adjusted according to the specific conditions of the error signal and the input signal. This adjustment process is divided into two stages. The first is a coarse adjustment stage. In the coarse adjustment stage, the weight coefficient of the adaptive filter is preliminarily adjusted to a state close to convergence, laying the foundation for subsequent fine adjustment. In the fine adjustment stage, the weight coefficient after the preliminary adjustment is adjusted more finely until the weight coefficient finally converges to the optimal solution, at which time the adjustment is stopped. The step of adjusting the weight coefficient of the adaptive filter according to the error signal and the input signal includes:

[0102] Set the initial weight coefficient and calculate the gradient vector based on the error signal and the input signal;

[0103] Multiply the gradient vector by the preset learning rate to get the adjustment amount of the weight coefficient;

[0104] The adjustment amount of the weight coefficient is added to the initial weight coefficient to obtain the updated weight coefficient, until the weight coefficient initially converges to a solution close to the optimal solution, completing the coarse adjustment stage;

[0105] The weight coefficient outputted in the coarse jump phase is recorded as the fine-tuning initial weight in the fine-tuning phase;

[0106] Perform adaptive evolution processing on the fine-tuned initial weights, and search for the global optimal solution by iteratively updating the covariance matrix of the fine-tuned initial weights;

[0107] In each iteration, a set of candidate weight coefficients is generated according to the current weight coefficients, and the fitness value of each candidate weight coefficient is calculated;

[0108] The candidate weight coefficient with the best fitness value is selected as the current weight coefficient for the next iteration, and the iterative process is repeated until the weight coefficient converges to the global optimal solution, completing the fine-tuning stage;

[0109] Specifically, when adjusting the weight coefficient of the adaptive filter, an initial weight coefficient is first set. The setting of the initial weight coefficient is usually determined based on experience or system default settings. Then, the gradient vector (T x =W d ×X s , where T x represents the gradient vector, W d represents the partial derivative of the error signal with respect to the weight coefficient, X s Represents the input signal, and the gradient vector reflects the direction and size of the weight coefficient adjustment), and then the gradient vector is multiplied by the preset learning rate to obtain the adjustment amount of the weight coefficient, so as to determine the adjustment amplitude, and then the adjustment amount of the weight coefficient is added to the initial weight coefficient to obtain the updated weight coefficient, where the update function of the updated weight coefficient is: ω(n+1)=ω(n)+2μX(n)T x , where ω(n) represents the weight coefficient of the adaptive filter at the nth iteration, and μ represents the convergence step. This process is repeated until the weight coefficient initially converges to a state close to the optimal solution. This stage is the coarse tuning stage. The main purpose is to quickly approach the optimal solution. After the coarse tuning stage is completed, the weight coefficient output in the coarse tuning stage is recorded as the initial fine-tuning weight in the fine-tuning stage, and after entering the fine-tuning stage, the initial fine-tuning weight is adaptively evolved. This process aims to search and approach the global optimal solution by continuously iteratively updating the covariance matrix of the initial fine-tuning weight. In each iteration, a set of candidate weight coefficients are generated according to the current weight coefficients, and the fitness value (S d =J L / Z L , where S d represents the fitness value, J L Indicates the improvement ratio of filtering effect, Z LIndicates the increase ratio of computing resource consumption), the fitness value is the basis for evaluating the quality of candidate weight coefficients. Finally, the one with the best fitness value is selected from the candidate weight coefficients as the current weight coefficient for the next iteration. The iterative update function involved in this iterative process is: ω′(m+1)=ω′(m)+μX(m)T′ x , where ω′(m) represents the weight coefficient of the filter at the mth iteration, X(m) represents the error signal of the filter, and T′ x Represents the gradient vector under the filter, and the iterative operation is repeated until the weight coefficient finally converges to the global optimal solution, thereby completing the fine-tuning stage of the adaptive filter. In addition, the weight coefficient output in the fine-tuning stage can also be fed back to the coarse-tuning stage as the initial weight coefficient for the next coarse-tuning stage, thereby achieving two-way optimization, thereby maintaining continuous optimization and updating of the weight coefficient.

[0110] Secondly, when the weight coefficient of the adaptive filter is adjusted, a variable step size strategy is also included for dynamically adjusting the step size factor. The specific process is as follows:

[0111] Obtain the instantaneous value and historical value of the error signal, as well as the gradient information of the current weight coefficient;

[0112] Calculating the rate of change of the error signal based on the instantaneous value and historical value of the error signal;

[0113] According to the rate of change of the error signal and the gradient information of the current weight coefficient, the corresponding step size factor is dynamically matched;

[0114] Among them, the step factor increases when the error signal changes greatly, and decreases when the error signal changes less;

[0115] Specifically, the weight coefficient of the adaptive filter is adjusted in the process, which involves not only the basic weight update mechanism, but also the variable step size strategy for dynamically adjusting the step size factor to ensure the optimal performance of the filter under different working conditions. First, the instantaneous value of the error signal, that is, the error data at the current moment, is obtained in real time. At the same time, the historical value of the error signal is also recorded and saved for subsequent comparison and analysis. In addition, the gradient information of the current weight coefficient is captured, and then the instantaneous value and historical value of the error signal are used to calculate the rate of change of the error signal (the rate of change of the error signal = the instantaneous value of the current error signal - the average value of the historical error signal / the standard deviation of the historical error signal) to reflect the dynamic change trend of the error signal at different time points. Then, based on the calculated rate of change of the error signal and combined with the gradient information of the current weight coefficient, the error signal is automatically adjusted. After comprehensive analysis, the most appropriate step factor can be dynamically matched. This step factor is not fixed, but will be flexibly adjusted according to the actual situation. The value range of the step factor is preferably between 0.01 and 0.1. The specific value is set according to the actual application scenario and system requirements. Specifically, when the rate of change of the error signal is large, indicating that the error signal fluctuates more violently, the step factor will be increased accordingly to adjust the weight coefficient more quickly, respond quickly to error changes, and improve the convergence speed of the filter. On the contrary, when the rate of change of the error signal is small, indicating that the error signal is relatively stable, the system will reduce the step factor to avoid oscillation and instability caused by excessive adjustment, ensure the stability and accuracy of the filter, so as to adapt to different signal environments more intelligently, realize fine adjustment of the weight coefficient, and significantly improve the filtering performance.

[0116] S5, performing signal merging processing on the signal to be processed after weight processing, restoring the original wireless signal, and outputting it to the wireless receiving device;

[0117] In step S5, the signals to be processed after weight coefficient optimization processing are combined to restore the original wireless signal, and output it to the wireless receiving device to complete the entire high dynamic wireless receiving process, wherein the step of combining the signals to be processed after weight processing to restore the original wireless signal includes:

[0118] Perform inverse Fourier transform on each signal to be processed after weight processing, convert the frequency domain signal back to the time domain, and obtain parallel signals in the time domain;

[0119] Perform time synchronization processing on the parallel signals in each time domain to determine the temporal consistency of each parallel signal;

[0120] By using the signal superposition method, the time-synchronized parallel signals are merged to obtain a merged composite signal;

[0121] Decoding the combined composite signal and restoring the original wireless signal according to the preset coding rules;

[0122] When the signal to be processed after weight processing is combined, first, for each signal to be processed that has been weighted, an inverse Fourier transform operation is performed, the purpose of which is to convert the frequency domain signal back to the time domain signal, thereby obtaining a parallel signal in the time domain. The inverse Fourier transform is a signal processing that can remap the information in the frequency domain to the time domain to ensure the integrity and accuracy of the signal. Then, the parallel signals in each time domain are subjected to time synchronization processing. Only by ensuring the consistency of each parallel signal in time can the accuracy and effectiveness of subsequent signal merging be guaranteed. The time synchronization processing eliminates the time difference between each signal through precise time calibration technology, so that they are aligned on the time axis. Then, the parallel signals after time synchronization processing are combined by signal superposition. By superimposing multiple signals together to form a composite signal, signal superposition can not only improve the overall strength of the signal, but also effectively reduce noise interference and improve signal quality. Finally, the combined composite signal is decoded, and the composite signal is parsed layer by layer according to the preset coding rules to finally restore the original wireless signal.

[0123] See also Figure 2 A high-dynamic wireless receiving system under multi-channel parallel operation, using the above-mentioned high-dynamic wireless receiving method under multi-channel parallel operation, comprises:

[0124] A signal segmentation module, which is used to segment the wireless signal to form multiple parallel signals, wherein each parallel signal corresponds to a communication channel;

[0125] A parameter adjustment module, the parameter adjustment module is used to count the error signal output from the wireless signal according to a preset expected response, and use the error signal to adjust the parameters of the adaptive filter;

[0126] A filtering module is used to filter each parallel signal through an adaptive filter, remove interference noise in the parallel signal, and output it as a signal to be processed;

[0127] A weight adjustment module, the weight adjustment module is used to adjust the weight coefficient of the adaptive filter according to the error signal and the input signal, wherein the weight coefficient adjustment includes a coarse adjustment stage and a fine adjustment stage;

[0128] In the coarse adjustment stage, the weight coefficients of the adaptive filter are initially adjusted to be close to convergence;

[0129] In the fine-tuning stage, the weight coefficients after the initial adjustment are fine-tuned until the weight coefficients converge to the optimal solution;

[0130] The signal merging module is used to perform signal merging processing on the signals to be processed after weight processing, restore the original wireless signals, and output them to the wireless receiving device.

[0131] In the above, the signal segmentation module is responsible for segmenting the input wireless signal and dividing it into multiple parallel signal paths to ensure that each parallel signal corresponds to an independent communication channel, thereby realizing parallel processing of the signal and improving the processing efficiency and response speed of the system. The parameter adjustment module counts and analyzes the output error signal in the wireless signal according to the preset expected response standard, and uses these error signals to dynamically adjust the various parameters of the adaptive filter to ensure that the filter can better adapt to the changes in the signal. The filtering module is responsible for filtering each parallel signal through an adaptive filter, effectively removing various interference noises in the parallel signal, purifying the signal quality, and outputting the filtered signal as a signal to be further processed. The task of the weight adjustment module is to adaptively adjust the weight coefficient of the adaptive filter according to the characteristics of the error signal and the input signal. This adjustment process is divided into two stages. The first is the coarse adjustment stage. In this stage, the weight coefficient of the adaptive filter is initially adjusted to a state close to the convergence state, laying the foundation for subsequent fine adjustment. The second is the fine adjustment stage. In this stage, the weight coefficient after coarse adjustment is finely adjusted until the weight coefficient converges to the optimal solution to ensure the best filtering effect. The signal merging module is responsible for merging the signals to be processed after fine processing of the weight coefficients, restoring the original wireless signal, and outputting it to the wireless receiving device to complete the entire high-dynamic wireless receiving process to ensure the stability and reliability of the received signal.

[0132] See also Figure 3 , an electronic device, the electronic device comprising:

[0133] at least one processor;

[0134] and a memory communicatively coupled to the at least one processor;

[0135] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned high-dynamic wireless receiving method under multi-path parallelism.

[0136] The processor of the above-mentioned electronic device can be a central processing unit (CPU), a graphics processing unit (GPU) or a digital signal processor (DSP), etc. These processors have powerful computing power and high-speed data processing capabilities, and can meet the real-time requirements of the high-dynamic wireless receiving method for complex signal processing. The memory is used to store computer programs and various data and information to ensure that the electronic device can quickly access and call the required data and programs when executing the high-dynamic wireless receiving method. When the computer program is executed by the processor, the processor can follow the program instructions to gradually complete a series of operations such as signal segmentation, parameter adjustment, filtering processing, weight adjustment and signal merging, and finally achieve stable reception of high-dynamic wireless signals. In addition, the electronic device may also include other hardware components, such as an operator, an input device, an output device and a network interface. The operator can be an arithmetic logic unit (ALU), which is responsible for performing various arithmetic and logical operations. The input device is such as a keyboard, a mouse or a touch screen, which is used to receive user instructions and input information; the output device is such as a display or a printer, which is used to display the processing results or print output information. The network interface is used to connect the electronic device with other network devices to achieve data transmission and communication.

[0137] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0138] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A high dynamic wireless receiving method under multi-path parallel, characterized in that: include: The wireless signal is segmented to form multiple parallel signals, wherein each parallel signal corresponds to a communication channel; According to a preset expected response, counting the error signal output in the wireless signal, and using the error signal to adjust the parameters of the adaptive filter; Each parallel signal is filtered by an adaptive filter to remove interference noise in the parallel signal and output as a signal to be processed; Adjusting the weight coefficient of the adaptive filter according to the error signal and the input signal, wherein the weight coefficient adjustment includes a coarse adjustment stage and a fine adjustment stage; In the coarse adjustment stage, the weight coefficients of the adaptive filter are preliminarily adjusted to be close to convergence; In the fine-tuning stage, the weight coefficient after the preliminary adjustment is fine-tuned until the weight coefficient converges to the optimal solution; The signals to be processed after weight processing are combined to restore the original wireless signals and output them to the wireless receiving device.

2. The high dynamic wireless receiving method under multi-path parallelism according to claim 1, characterized in that: The step of dividing the wireless signal to form multiple parallel signals includes: Acquire an original wireless signal, and a signal strength and frequency of the original wireless signal; Obtaining the required number of segmentations of the original wireless signal, and determining the bandwidth and modulation mode of each communication channel according to the signal strength and frequency; According to the required number of segmentations, the original wireless signal is segmented using Fourier transform, the signal is converted into the frequency domain, multiple parallel signals are obtained, and each parallel signal is then allocated to the corresponding communication channel.

3. The high dynamic wireless receiving method under multi-path parallelism according to claim 1, characterized in that: The step of counting the error signal output from the wireless signal according to the preset expected response includes: Obtaining a preset expected response signal; Calculating the difference between the wireless signal and the expected response signal to obtain an error signal, and calculating the occurrence frequency and error amplitude of the error signal; Determining a risk score of the error signal according to the frequency of occurrence and the error amplitude of the error signal, and determining whether the error signal is added to the parameter adjustment process of the adaptive filter according to the risk score; The error signal is used to adjust the parameters of the adaptive filter to reduce the error between the signal to be processed and the expected response signal.

4. The high dynamic wireless receiving method under multi-path parallelism according to claim 1, characterized in that: The step of determining a risk score of the error signal according to the occurrence frequency and error amplitude of the error signal, and determining whether to add the error signal to the parameter adjustment process of the adaptive filter according to the risk score, comprises: Obtaining the occurrence frequency and error amplitude of the error signal, and recording them as a first evaluation parameter and a second evaluation parameter respectively; Obtaining an evaluation function, and inputting the first evaluation parameter and the second evaluation parameter into the evaluation function together, and recording an output result of the evaluation function as a risk score; Obtaining a preset risk threshold, and comparing the risk threshold with the risk score; When the risk score is greater than or equal to a risk threshold, adding the error signal to a parameter adjustment process of an adaptive filter; When the risk score is less than the risk threshold, the error signal is ignored and no adjustment is made to the adaptive filter parameters.

5. The high dynamic wireless receiving method under multi-path parallelism according to claim 1, characterized in that: The step of filtering each parallel signal by using an adaptive filter comprises: Obtaining known interference noise characteristics and an error signal added to a parameter adjustment process of an adaptive filter; Setting initial filtering parameters of the adaptive filter according to the interference noise characteristics and an error signal added to the parameter adjustment process of the adaptive filter; The parallel signals are input into an adaptive filter for processing to remove interference noise in the parallel signals.

6. The high dynamic wireless receiving method under multi-path parallelism according to claim 1, characterized in that: The step of adjusting the weight coefficient of the adaptive filter according to the error signal and the input signal comprises: Set the initial weight coefficient and calculate the gradient vector based on the error signal and the input signal; Multiplying the gradient vector by a preset learning rate to obtain an adjustment amount of the weight coefficient; Adding the adjustment amount of the weight coefficient to the initial weight coefficient to obtain an updated weight coefficient, until the weight coefficient initially converges to a solution close to the optimal solution, completing the coarse adjustment stage; The weight coefficient outputted in the coarse jump phase is recorded as the fine-tuning initial weight in the fine-tuning phase; Performing adaptive evolution processing on the fine-tuning initial weights, searching for a global optimal solution by iteratively updating the covariance matrix of the fine-tuning initial weights; In each iteration, a set of candidate weight coefficients is generated according to the current weight coefficient, and the fitness value of each candidate weight coefficient is calculated; The candidate weight coefficient with the best fitness value is selected as the current weight coefficient for the next iteration, and the iterative process is repeated until the weight coefficient converges to the global optimal solution, completing the fine-tuning stage.

7. The high dynamic wireless receiving method under multi-path parallelism according to claim 1, characterized in that: When the weight coefficient of the adaptive filter is adjusted, a variable step size strategy for dynamically adjusting the step size factor is also included, and the specific process is as follows: Obtain the instantaneous value and historical value of the error signal, as well as the gradient information of the current weight coefficient; Calculating the rate of change of the error signal according to the instantaneous value and the historical value of the error signal; Dynamically matching the corresponding step size factor according to the rate of change of the error signal and the gradient information of the current weight coefficient; The step factor increases when the error signal changes greatly, and decreases when the error signal changes less.

8. The high dynamic wireless receiving method under multi-path parallelism according to claim 1, characterized in that: The step of performing signal merging processing on the weighted signals to be processed to restore the original wireless signals comprises: Perform inverse Fourier transform on each signal to be processed after weight processing, convert the frequency domain signal back to the time domain, and obtain parallel signals in the time domain; Perform time synchronization processing on the parallel signals in each time domain to determine the temporal consistency of each parallel signal; By using the signal superposition method, the time-synchronized parallel signals are merged to obtain a merged composite signal; The combined composite signal is decoded and the original wireless signal is restored according to the preset coding rules.

9. A high dynamic wireless receiving system under multi-channel parallel operation, characterized in that: The high dynamic wireless receiving method under multi-path parallelism described in any one of claims 1 to 8 comprises: A signal segmentation module, wherein the signal segmentation module is used to segment the wireless signal to form multiple parallel signals, wherein each parallel signal corresponds to a communication channel; A parameter adjustment module, the parameter adjustment module is used to count the error signal output from the wireless signal according to a preset expected response, and use the error signal to adjust the parameters of the adaptive filter; A filtering module, wherein the filtering module is used to filter each parallel signal through an adaptive filter, remove interference noise in the parallel signal, and output it as a signal to be processed; A weight adjustment module, the weight adjustment module is used to adjust the weight coefficient of the adaptive filter according to the error signal and the input signal, wherein the weight coefficient adjustment includes a coarse adjustment stage and a fine adjustment stage; In the coarse adjustment stage, the weight coefficients of the adaptive filter are preliminarily adjusted to be close to convergence; In the fine-tuning stage, the weight coefficient after the preliminary adjustment is fine-tuned until the weight coefficient converges to the optimal solution; The signal merging module is used to perform signal merging processing on the signals to be processed after weight processing, restore the original wireless signals, and output them to the wireless receiving device.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the high dynamic wireless reception method under multi-path parallelism as described in any one of claims 1 to 8.

Images