Joint estimation method for time difference and frequency difference among multiple antenna signals of distributed system

Abstract

The embodiment of the invention relates to the technical field of communication, in particular to a time difference and frequency difference joint estimation method for multi-antenna signals of a distributed system, and the method comprises the steps: carrying out the optimization solution of a variational mode decomposition algorithm through employing a QPSO algorithm, and obtaining the optimal solution of a decomposition layer number and the optimal solution of a penalty factor, performing variational mode decomposition on the received signal based on the optimal solution of the decomposition layer number and the optimal solution of the penalty factor to obtain a plurality of IMF components; the variance contribution rate of each IMF component is calculated, and the IMF components with the variance contribution rates smaller than a first preset threshold value are filtered out; performing wavelet threshold processing on each reserved IMF component; and performing signal reconstruction based on each IMF component after wavelet threshold processing, calculating a conjugate fuzzy function of a reconstructed received signal, performing peak detection on the conjugate fuzzy function, and estimating to obtain a time delay difference and a frequency difference among multiple antenna signals of the distributed system. The method can realize high-precision time difference and frequency difference joint estimation.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of communication technology, and in particular to a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system. Background Art

[0002] The antenna arrays of the receiving devices in the distributed system are randomly arranged, the array shapes are irregular, the antenna types, directions and receiver channel characteristics are different, and the existence of Doppler frequency shift will affect the fluctuation of the center frequency of the received signal. Therefore, there may be obvious arrival time differences and frequency differences between different array elements of the receiving devices in the distributed system. How to estimate and compensate for the above differences is the key and difficulty to effectively achieve synthesis.

[0003] At present, domestic and foreign research teams have proposed some methods for estimating and compensating the delay difference and frequency difference between signals. One of the methods is to estimate and compensate the delay difference and frequency difference separately. Traditional delay difference estimation methods include: delay estimation method based on blind separation technology, variable step size LMS algorithm, generalized quadratic cross-correlation algorithm based on sparse Fourier transform, minimum mean square delay difference estimation algorithm, etc. For the estimation and compensation of frequency difference between signals, the general method is to approximate the single-tone sinusoidal signal by conjugate point multiplication. At this time, the frequency of the sinusoidal signal is the frequency difference between the two signals, which is converted into the frequency estimation problem of the single-tone sinusoidal signal. Traditional frequency difference estimation methods include: sinusoidal signal frequency estimation algorithm based on fast Fourier transform and discrete time Fourier transform interpolation, FFT frequency deviation estimation algorithm based on interpolation and binary search, improved Rife algorithm, frequency deviation fine estimation algorithm based on reference signal construction and binary search, etc. However, the above-mentioned methods all adopt the method of independent estimation and compensation of parameter differences, which may bring additional algorithm overhead due to parameter decoupling, and even have a negative impact on parameter estimation performance.

[0004] Another estimation method is the joint estimation method of time-frequency difference based on the dual relationship between arrival time difference and frequency difference. The mutual fuzzy function in time-frequency analysis technology is a classic algorithm for joint estimation of time-frequency difference. It can obtain accurate estimation under the premise of ensuring computational complexity under high signal-to-noise ratio and high sampling rate conditions. However, since this method is solved by grid search in parameter space, only estimates of integer multiples of sampling intervals can be obtained. In addition, there are methods based on the maximum likelihood joint estimation model of importance sampling method, joint estimation algorithms of time difference and frequency difference for sparse signals in frequency domain, methods based on the improved maximum likelihood joint estimation model of Markov chain Monte Carlo method, and joint estimation algorithms of time-frequency difference based on fractional low-order cyclostationarity.

[0005] The above-mentioned time-frequency difference estimation methods are mostly simulated and designed under non-correlated noise. However, the actual communication environment is very complex nowadays and there is the influence of correlated noise. The use of the above-mentioned methods may lead to inaccurate estimation results.

[0006] Through the above analysis, the time difference and frequency difference estimation methods currently proposed still have the following defects.

[0007] First, most of the current distributed signal parameter difference estimation technologies focus on independent estimation of time difference and frequency difference, which does not fully utilize the characteristics of collaborative reception of distributed systems, nor does it fully utilize the time-frequency duality relationship between received signals.

[0008] Second, most of the current methods for estimating parameter differences between distributed signals consider the influence of uncorrelated noise, and lack research on the joint estimation of time-frequency differences under the influence of uncorrelated noise. Summary of the invention

[0009] In order to solve the above technical problems, an embodiment of the present application proposes a method for jointly estimating time differences and frequency differences between multi-antenna signals in a distributed system, which can effectively avoid the problem of under-decomposition or over-decomposition of signals during variational mode decomposition, and can effectively avoid the false filtering out of useful signals, and has a good denoising effect, thereby realizing high-precision joint estimation of time differences and frequency differences.

[0010] To achieve the above-mentioned purpose, an embodiment of the present application provides a method for jointly estimating time difference and frequency difference between multi-antenna signals in a distributed system, and the method comprises the following steps: optimizing and solving a variational mode decomposition algorithm using a QPSO algorithm to obtain an optimal solution for the number of decomposition layers and an optimal solution for a penalty factor, performing variational mode decomposition on a received signal based on the optimal solution for the number of decomposition layers and the optimal solution for the penalty factor to obtain multiple IMF components; calculating the variance contribution rate of each IMF component respectively, and filtering out the IMF components whose variance contribution rate is less than a first preset threshold; performing wavelet threshold processing on the retained IMF components; performing signal reconstruction based on the IMF components after wavelet threshold processing, calculating the conjugate fuzzy function of the reconstructed received signal, and performing peak detection on the conjugate fuzzy function to estimate the time delay difference and frequency difference between multi-antenna signals in the distributed system.

[0011] To achieve the above-mentioned purpose, an embodiment of the present application also provides a system for jointly estimating time difference and frequency difference between multi-antenna signals in a distributed system, the system comprising: an IMF component acquisition module, used to optimize and solve the variational mode decomposition algorithm using a QPSO algorithm, obtain the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor, and perform variational mode decomposition on the received signal based on the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor to obtain multiple IMF components; an IMF component filtering module, used to calculate the variance contribution rate of each IMF component respectively, and filter out the IMF components whose variance contribution rate is less than a first preset threshold; a wavelet threshold processing module, used to perform wavelet threshold processing on each retained IMF component; a time difference and frequency difference joint estimation module, used to perform signal reconstruction based on each IMF component after wavelet threshold processing, calculate the conjugate fuzzy function of the reconstructed received signal, and perform peak detection on the calculated conjugate fuzzy function to estimate the time delay difference and frequency difference between multi-antenna signals in the distributed system.

[0012] To achieve the above-mentioned purpose, an embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system as described above.

[0013] To achieve the above objectives, an embodiment of the present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system as described above.

[0014] In some optional embodiments, before using the QPSO algorithm to optimize and solve the variational mode decomposition algorithm, the method further includes:

[0015] Two received signals are obtained and a signal model is established for the two received signals. There is both a delay difference and a frequency difference between the two received signals. The established signal model is expressed by the formula:

[0016]

[0017] Wherein, x1(t) represents the first received signal, x2(t) represents the second received signal, a1 represents the amplitude attenuation of the first received signal during the propagation process, a2 represents the amplitude attenuation of the second received signal during the propagation process, s(t) represents the useful signal radiated by the radiation source, τ1 represents the time delay of the first received signal transmission, τ2 represents the time delay of the second received signal transmission, n1(t) represents the additive Gaussian noise added to the first received signal, and n2(t) represents the additive Gaussian noise added to the second received signal;

[0018] The first received signal x1(t) is used as the reference signal to simplify the signal model. The simplified signal model is expressed by the formula:

[0019]

[0020] A=a1 / a2;

[0021] D = τ2-τ1;

[0022] Wherein, A is the attenuation coefficient ratio, D is the time delay difference between the two received signals to be determined, and Δf is the frequency difference between the two received signals to be determined.

[0023] In some optional embodiments, after simplifying the signal model, the method further includes:

[0024] The fitness function is designed as the ratio of the envelope entropy and the kurtosis value;

[0025] The calculation formula of envelope entropy is:

[0026]

[0027] Among them, E p represents the envelope entropy, K is the total number of IMF components obtained by variational mode decomposition, and a(k) represents the envelope signal of the kth IMF component obtained by variational mode decomposition after Hilbert demodulation;

[0028] The calculation formula of kurtosis value is:

[0029]

[0030] Among them, K4 represents the kurtosis value, n represents the total number of discrete points in the discrete sequence, and x t It represents the vibration amplitude corresponding to the tth discrete point in the discrete sequence of the time domain waveform, and μ represents the average amplitude of the discrete sequence.

[0031] In some optional embodiments, the QPSO algorithm is used to optimize and solve the variational mode decomposition algorithm to obtain the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor, and the received signal is subjected to variational mode decomposition based on the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor to obtain multiple IMF components, which is achieved by the following steps:

[0032] S11, taking the decomposition number K and the penalty factor α as optimization variables, and setting the search range of the decomposition number K and the search range of the penalty factor α;

[0033] S12, set the population size M and the spatial search dimension R, initialize the population particles, and set the number of iterations at this time to t = 1;

[0034] S13, based on the designed fitness function, calculate the current fitness value of the particle. If the current fitness value is better than the fitness value of the previous iteration, the current position of the particle is used as the individual optimal position of the particle. Otherwise, the individual optimal position of the particle is not updated. The update of the individual optimal position is achieved by the following formula:

[0035]

[0036] in, is a random number between [0,1] and follows a normal distribution. represents the individual optimal position of the i-th particle after updating at the t-th iteration, p id (t) represents the individual optimal position of the i-th particle before updating at the t-th iteration, p gd (t) represents the global optimal position;

[0037] S14, comparing the fitness values ​​of all particles in the population with the global optimal position respectively, if the fitness value of a particle is better than the fitness value of the global optimal position, then the individual optimal position of the example whose fitness value is better than the fitness value of the global optimal position is taken as the global optimal position;

[0038] S15, calculate the average optimal position of the particle swarm, the average optimal position of the particle swarm is calculated by the following formula:

[0039]

[0040] in, represents the average optimal position of the particle swarm;

[0041] S16, calculating the potential well center of the non-optimal particle for each dimension of the particle, and calculating the position of a random point;

[0042] S17, updating the particle position, calculating the new position of the particle in each dimensional space, the new position of the particle in each dimensional space is calculated by the following formula;

[0043]

[0044]

[0045] Among them, β is the shrinkage expansion coefficient, η and ζ are the shrinkage expansion coefficient parameters, t max is the maximum number of iterations, X id (t) represents the new position of the i-th particle at the completion of the t-1th iteration, X id (t+1) represents the new position of the i-th particle at the completion of the t-th iteration;

[0046] S18, update the number of iterations plus one, repeat S53 to S57, if the fitness decreases, update the number of decomposition layers K and the penalty factor α until the maximum number of iterations t is reached max , output the tth max The global optimal value obtained after iterations is used as the optimal solution for the number of decomposition layers K and the optimal solution for the penalty factor α;

[0047] S19, performing variational mode decomposition on the received signal based on the optimal solution of the decomposition layer number K and the optimal solution of the penalty factor α to obtain multiple IMF components.

[0048] In some optional embodiments, the variance contribution rate of each IMF component is calculated separately, which is implemented by the following formula:

[0049]

[0050] Where K represents the total number of IMF components obtained by variational mode decomposition of the received signal, u k (t) represents the kth IMF component, VCR k Represents the variance contribution rate of the kth IMF component.

[0051] In some optional embodiments, wavelet threshold processing is performed on each retained IMF component, including:

[0052] Select appropriate wavelet basis to perform j-layer wavelet decomposition on each IMF component;

[0053] Perform adaptive wavelet threshold function processing at each scale to estimate the wavelet coefficients;

[0054] The estimated wavelet coefficients are reconstructed and denoised to obtain the IMF components after wavelet threshold processing, that is, the denoised IMF components;

[0055] The expression of the adaptive wavelet threshold function is as follows:

[0056]

[0057]

[0058] in, represents the wavelet threshold function, λ represents the adaptive threshold, σ represents the noise variance, N represents the signal length, λ j represents the wavelet decomposition threshold of the jth layer, r 1,2j represents the correlation strength between the j-th layer wavelet decomposition coefficient and the original signal coefficient, a2 represents the original signal data, a 2,j represents the signal data after the j-th layer wavelet decomposition, Cov(·,·) represents the calculated covariance, Indicates the calculation of variance.

[0059] In some optional embodiments, the conjugate ambiguity function of the reconstructed received signal is calculated, and the conjugate ambiguity function is peak detected to estimate the time delay difference and frequency difference between the multi-antenna signals of the distributed system, which is achieved by the following formula:

[0060]

[0061]

[0062] in, represents the reconstructed first received signal, represents the reconstructed second received signal, represents the conjugate ambiguity function of the reconstructed two-path received signals, and (D, Δf) represents the estimated delay difference and frequency difference between the multi-antenna signals of the distributed system.

[0063] The embodiment of the present application proposes a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system, which has the following technical effects.

[0064] First, the QPSO algorithm was used to optimize the variational mode decomposition algorithm, and the optimal solutions for the number of decomposition layers and the penalty factor were obtained. The received signal was subjected to variational mode decomposition using the optimal solution, thereby obtaining multiple IMF components. The variance contribution rate of each IMF component was calculated, and the IMF components with too small variance contribution rate were filtered out, which was able to transmit the characteristics of the original signal with less information loss. The remaining IMF components were subjected to wavelet threshold processing, which included selecting wavelet functions for wavelet decomposition and adaptive threshold denoising. The signal was reconstructed, the conjugate fuzzy function of the reconstructed signal was calculated, and the time delay difference and frequency difference between signals were estimated through peak detection, filling the gap in this field.

[0065] Second, the ratio of envelope entropy and kurtosis value is selected as the fitness function. By searching the global minimum fitness value, the optimal combination of decomposition layers and penalty factors is quickly and accurately determined and used for variational mode decomposition. This can effectively avoid under-decomposition or over-decomposition of the signal during variational mode decomposition.

[0066] Third, an adaptive wavelet threshold function is designed in the wavelet denoising process. On this basis, in order to more accurately select the threshold to adapt to the characteristics of the noise coefficient change, a new threshold that changes with the number of decomposition layers is further proposed. It can adaptively select the appropriate threshold according to the correlation coefficient between the data after each layer of decomposition and the original data, which conforms to the characteristic of small modulus value of noise signal under high-level decomposition, effectively avoids the false filtering of useful signals, and achieves better denoising effect.

[0067] In summary, the proposed method for joint estimation of time and frequency differences between multi-antenna signals in a distributed system emphasizes the importance of solving the problem of parameter acquisition of the variational mode decomposition algorithm using quantum particle swarm optimization in the joint estimation algorithm of time and frequency differences. Any joint estimation task of time and frequency differences based on QPSO optimization under the influence of correlated noise can be jointly estimated using this method. This method has good denoising effect and universality, and can achieve high-precision joint estimation of time and frequency differences. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related technologies, the drawings required for use in the embodiments of the present application or the related technical descriptions will be briefly introduced below. Obviously, the following drawings are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. The drawings described here are only used to explain the present application and are not used to limit the present application.

[0069] Figure 1 It is a flow chart of a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system proposed in one embodiment of the present application;

[0070] Figure 2 A comparison diagram of the root mean square error of delay difference estimation between a time difference and frequency difference joint estimation method between multi-antenna signals in a distributed system and a traditional time and frequency difference joint estimation method is provided in one embodiment of the present application;

[0071] Figure 3 A comparison diagram of the root mean square error of frequency difference estimation between a time difference and frequency difference joint estimation method between multi-antenna signals in a distributed system and a traditional time and frequency difference joint estimation method is provided in one embodiment of the present application;

[0072] Figure 4is a structural schematic diagram of a system for jointly estimating time and frequency differences between multi-antenna signals in a distributed system provided in another embodiment of the present application;

[0073] Figure 5 It is a structural schematic diagram of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. It will be appreciated by those skilled in the art that in the embodiments of the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be limited in any way. The various embodiments can be combined and referenced with each other without contradiction.

[0075] An embodiment of the present application proposes a method for jointly estimating the time difference and frequency difference between multi-antenna signals in a distributed system, which is applied to an electronic device, wherein the electronic device may be a terminal or a server. This embodiment and the following embodiments are all described using the server as an example. The implementation details of the method for jointly estimating the time difference and frequency difference between multi-antenna signals in a distributed system proposed in this embodiment are specifically described below. The following content is only the implementation details provided for ease of understanding and is not necessary for implementing this solution.

[0076] The specific process of the method for jointly estimating the time difference and frequency difference between multiple antenna signals in a distributed system proposed in this embodiment can be as follows: Figure 1 As shown, including:

[0077] S1, the QPSO algorithm is used to optimize the variational mode decomposition algorithm to obtain the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor. Based on the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor, the received signal is subjected to variational mode decomposition to obtain multiple IMF components.

[0078] In the specific implementation, after obtaining the received signal, the server needs to use the QPSO algorithm to optimize and solve the variational mode decomposition algorithm to obtain the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor. Based on the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor, the received signal is subjected to variational mode decomposition to obtain multiple IMF components. The QPSO algorithm can quickly and accurately obtain the optimal combination of the number of decomposition layers and the penalty factor, thereby performing the optimal variational mode decomposition.

[0079] In one example, the server considers the situation where there is both a delay difference and a frequency difference between two received signals, and establishes a signal model for the two received signals. The established signal model is expressed by the formula:

[0080]

[0081] Among them, x1(t) represents the first received signal, x2(t) represents the second received signal, a1 represents the amplitude attenuation of the first received signal during the propagation process, a2 represents the amplitude attenuation of the second received signal during the propagation process, s(t) represents the useful signal radiated by the radiation source, τ1 represents the time delay of the first received signal transmission, τ2 represents the time delay of the second received signal transmission, t∈[0,T), used to represent the observation space, n1(t) represents the additive Gaussian noise added to the first received signal, and n2(t) represents the additive Gaussian noise added to the second received signal. The power of n1(t) and n2(t) are respectively and n1(t) and n2(t) can be correlated or uncorrelated.

[0082] Furthermore, the first received signal x1(t) is used as a reference signal to simplify the established signal model. The simplified signal model can be expressed by the formula:

[0083]

[0084] A=a1 / a2;

[0085] D = τ2-τ1;

[0086] Wherein, A is the attenuation coefficient ratio, D is the delay difference between the two received signals to be determined, and Δf is the frequency difference between the two received signals to be determined. In the subsequent joint estimation of the time difference and frequency difference, only the value of D and the value of Δf need to be estimated.

[0087] In one example, a fitness function needs to be set in the QPSO algorithm, and the server designs the fitness function as a ratio of the envelope entropy to the kurtosis value.

[0088] In one example, the envelope entropy is calculated as:

[0089]

[0090]

[0091] Among them, E p represents the envelope entropy, H is the total number of IMF components obtained by variational mode decomposition, and a(h) represents the envelope signal of the h-th IMF component obtained by variational mode decomposition after Hilbert demodulation.

[0092] In one example, the kurtosis value is calculated as:

[0093]

[0094] Among them, K4 represents the kurtosis value, n represents the total number of discrete points in the discrete sequence, and x t It represents the vibration amplitude corresponding to the tth discrete point in the discrete sequence of the time domain waveform, and μ represents the average amplitude of the discrete sequence.

[0095] In one example, after completing the construction of the signal model and the design of the fitness function, the QPSO algorithm can be used to optimize the variational mode decomposition algorithm. Considering the time cost, the server sets the search range of the decomposition layer K to K∈[3,10], and sets the search range of the penalty factor α to α∈[100,2500].

[0096] The server uses the QPSO algorithm to optimize the variational mode decomposition algorithm to obtain the optimal solution for the number of decomposition layers and the optimal solution for the penalty factor. Based on the optimal solution for the number of decomposition layers and the optimal solution for the penalty factor, the received signal is subjected to variational mode decomposition to obtain multiple IMF components. This is achieved through the following steps:

[0097] S11, taking the decomposition number K and the penalty factor α as optimization variables, and setting the search range of the decomposition number K and the search range of the penalty factor α.

[0098] S12, set the population size M and the spatial search dimension R, initialize the population particles, and set the number of iterations at this time to t=1.

[0099] S13, based on the designed fitness function, calculate the current fitness value of the particle. If the current fitness value is better than the fitness value of the previous iteration, the current position of the particle is used as the individual optimal position of the particle. Otherwise, the individual optimal position of the particle is not updated. The update of the individual optimal position is achieved by the following formula:

[0100]

[0101] in, is a random number between [0,1] and follows a normal distribution. represents the individual optimal position of the i-th particle after updating at the t-th iteration, p id (t) represents the individual optimal position of the i-th particle before updating at the t-th iteration, p gd (t) represents the global optimal position.

[0102] S14, comparing the fitness values ​​of all particles in the population with the global optimal position respectively. If the fitness value of a particle is better than the fitness value of the global optimal position, the individual optimal position of the example whose fitness value is better than the fitness value of the global optimal position is taken as the global optimal position.

[0103] S15, calculate the average optimal position of the particle swarm, the average optimal position of the particle swarm is calculated by the following formula:

[0104]

[0105] in, Represents the average optimal position of the particle swarm.

[0106] S16, calculating the potential well center of the non-optimal particle for each dimension of the particle, and calculating the position of a random point.

[0107] S17, updating the particle position, calculating the new position of the particle in each dimensional space, the new position of the particle in each dimensional space is calculated by the following formula;

[0108]

[0109] Among them, β is the shrinkage expansion coefficient, η and ζ are the shrinkage expansion coefficient parameters, t max is the maximum number of iterations, X id (t) represents the new position of the i-th particle at the completion of the t-1th iteration, X id (t+1) represents the new position of the i-th particle when the t-th iteration is completed. When β>0.5, the ± in the above formula will be +, and when β≤0.5, the ± in the above formula will be -.

[0110] S18, update the number of iterations plus one, repeat S53 to S57, if the fitness decreases, update the number of decomposition layers K and the penalty factor α until the maximum number of iterations t is reached max , output the βth max The global optimal value obtained after iterations is taken as the optimal solution for the number of decomposition layers K and the optimal solution for the penalty factor α.

[0111] S19, performing variational mode decomposition on the received signal based on the optimal solution of the decomposition layer number K and the optimal solution of the penalty factor α to obtain multiple IMF components.

[0112] The server performs variational mode decomposition on the received signal based on the optimal solution of the decomposition layer number K and the optimal solution of the penalty factor α, and can decompose the received signal into K IMF components.

[0113] S2, respectively calculating the variance contribution rate of each IMF component, and filtering out the IMF components whose variance contribution rate is less than a first preset threshold.

[0114] In a specific implementation, after completing the variational mode decomposition of the received signal and obtaining K IMF components, the server can calculate the variance contribution rate of each IMF component respectively, and filter out the IMF component whose variance contribution rate is less than a first preset threshold. The first preset threshold can be set by a technician in this field according to actual needs.

[0115] In one example, the server calculates the variance contribution rate of each IMF component separately, which can be achieved by the following formula:

[0116]

[0117] Where K represents the total number of IMF components obtained by variational mode decomposition of the received signal, u k (t) represents the kth IMF component, VCR k Represents the variance contribution rate of the kth IMF component.

[0118] In one example, the first preset threshold is set to 0.01, and the server sends the VCR k The IMF components with values ​​< 0.01 are filtered out, and the remaining (remaining) IMF components are denoised.

[0119] S3, performing wavelet threshold processing on each retained IMF component.

[0120] In a specific implementation, the wavelet threshold processing process is the denoising process, and the server performs wavelet threshold processing on each retained IMF component in turn to obtain each IMF component after the wavelet threshold processing, that is, each IMF component after denoising.

[0121] In an example, the server first needs to select a suitable wavelet basis to perform j-layer wavelet decomposition on each IMF component, then perform adaptive wavelet threshold function processing at each scale to estimate the wavelet coefficients, and finally reconstruct and denoise the estimated wavelet coefficients to obtain the IMF components after wavelet threshold processing, that is, the denoised IMF components.

[0122] In one example, the expression of the adaptive wavelet threshold function is as follows:

[0123]

[0124] in, represents the wavelet threshold function, λ represents the adaptive threshold, the selection of λ is very critical, σ represents the noise variance, σ=median(|w j,k |) / 0.6745, median(|w j,k |) is the median of the original signal, N is the signal length, λj represents the wavelet decomposition threshold of the jth layer, λ j is a monotonically decreasing function. The larger the number of decomposition layers, the larger the j The smaller the value of 1,2j represents the correlation strength between the j-th layer wavelet decomposition coefficient and the original signal coefficient. Assume that a1 represents the original signal data, a 2,j represents the signal data after the j-th layer wavelet decomposition, Cov(·,·) represents the calculated covariance, Indicates the calculation of variance, |r 1,2j |<1.

[0125] S4, reconstructing the signal based on each IMF component after wavelet threshold processing, calculating the conjugate ambiguity function of the reconstructed received signal, and performing peak detection on the conjugate ambiguity function to estimate the delay difference and frequency difference between the multi-antenna signals of the distributed system.

[0126] In a specific implementation, after the server obtains the IMF components after wavelet threshold processing, it can reconstruct the signal based on the IMF components after wavelet threshold processing, calculate the conjugate ambiguity function of the reconstructed received signal, and perform peak detection on the conjugate ambiguity function to estimate the delay difference and frequency difference between the multi-antenna signals of the distributed system.

[0127] In one example, the server calculates the conjugate ambiguity function of the reconstructed received signal, performs peak detection on the conjugate ambiguity function, and estimates the delay difference and frequency difference between the multi-antenna signals of the distributed system, which is achieved by the following formula:

[0128]

[0129] in, represents the reconstructed first received signal, represents the reconstructed second received signal, represents the conjugate ambiguity function of the reconstructed two-path received signals, and (D, Δf) represents the estimated delay difference and frequency difference between the multi-antenna signals of the distributed system.

[0130] This embodiment proposes a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system, which has the following technical effects.

[0131] First, the QPSO algorithm was used to optimize the variational mode decomposition algorithm, and the optimal solutions for the number of decomposition layers and the penalty factor were obtained. The received signal was subjected to variational mode decomposition using the optimal solution, thereby obtaining multiple IMF components. The variance contribution rate of each IMF component was calculated, and the IMF components with too small variance contribution rate were filtered out, which was able to transmit the characteristics of the original signal with less information loss. The remaining IMF components were subjected to wavelet threshold processing, which included selecting wavelet functions for wavelet decomposition and adaptive threshold denoising. The signal was reconstructed, the conjugate fuzzy function of the reconstructed signal was calculated, and the time delay difference and frequency difference between signals were estimated through peak detection, filling the gap in this field.

[0132] Second, the ratio of envelope entropy and kurtosis value is selected as the fitness function. By searching the global minimum fitness value, the optimal combination of decomposition layers and penalty factors is quickly and accurately determined and used for variational mode decomposition. This can effectively avoid under-decomposition or over-decomposition of the signal during variational mode decomposition.

[0133] Third, an adaptive wavelet threshold function is designed in the wavelet denoising process. On this basis, in order to more accurately select the threshold to adapt to the characteristics of the noise coefficient change, a new threshold that changes with the number of decomposition layers is further proposed. It can adaptively select the appropriate threshold according to the correlation coefficient between the data after each layer of decomposition and the original data, which conforms to the characteristic of small modulus value of noise signal under high-level decomposition, effectively avoids the false filtering of useful signals, and achieves better denoising effect.

[0134] In summary, the proposed method for joint estimation of time and frequency differences between multi-antenna signals in a distributed system emphasizes the importance of solving the problem of parameter acquisition of the variational mode decomposition algorithm using quantum particle swarm optimization in the joint estimation algorithm of time and frequency differences. Any joint estimation task of time and frequency differences based on QPSO optimization under the influence of correlated noise can be jointly estimated using this method. This method has good denoising effect and universality, and can achieve high-precision joint estimation of time and frequency differences.

[0135] The steps of the above methods are divided only for the purpose of clear description. They can be combined into one step or some steps can be decomposed into multiple steps during implementation. As long as they include the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but not changing the core design of the algorithm and process, are all within the scope of protection of this application.

[0136] In one embodiment, in order to evaluate the performance of a method for jointly estimating the time and frequency differences between multiple antenna signals in a distributed system proposed in this application (hereinafter referred to as this method), we conducted relevant simulation experiments. In the simulation experiment, the specific parameters set are as follows: Matlab software is used to simulate the BPSK signal, the carrier frequency f c is 0.5MHz, the sampling rate f s The frequency difference D is set to 0.5 ms, the additional noise n1(t) and n2(t) of the signal are related, and the signal-to-noise ratio range is set to -15 dB to 5 dB. Monte Carlo simulations are performed 1000 times in each experiment to verify the method and compare its performance with the traditional joint estimation method.

[0137] The root mean square error of the delay difference estimation of this method is compared with the traditional time-frequency difference joint estimation method (such as the second-order mutual fuzzy algorithm and the fourth-order cumulant algorithm). Figure 2 As shown, the root mean square error of the frequency difference estimation is Figure 3 As shown in the figure, it can be seen that this method is feasible and can handle the delay difference and frequency difference estimation problems under the condition of correlated noise pollution. Under the same simulation conditions, the root mean square error of this method is smaller than that of several other methods, and the estimation performance is better. Therefore, the estimation performance of this method is better than that of traditional methods, and the simulation experiment verifies the creativity of this method.

[0138] Another embodiment of the present application proposes a system for jointly estimating the time difference and frequency difference between multiple antenna signals in a distributed system. The details of the system for jointly estimating the time difference and frequency difference between multiple antenna signals in a distributed system proposed in this embodiment are described in detail below. The following content is only the implementation details provided for easy understanding and is not necessary for the implementation of this example.

[0139] Figure 4 It is a structural diagram of a system for jointly estimating time and frequency differences between multi-antenna signals in a distributed system proposed in this embodiment. The system includes: an IMF component acquisition module M1, an IMF component filtering module M2, a wavelet threshold processing module M3 and a joint time and frequency difference estimation module M4.

[0140] The IMF component acquisition module M1 is used to optimize and solve the variational mode decomposition algorithm using the QPSO algorithm to obtain the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor, and perform variational mode decomposition on the received signal based on the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor to obtain multiple IMF components.

[0141] The IMF component filtering module M2 is used to calculate the variance contribution rate of each IMF component respectively, and filter out the IMF component whose variance contribution rate is less than a first preset threshold.

[0142] The wavelet threshold processing module M3 is used to perform wavelet threshold processing on each retained IMF component.

[0143] The time difference and frequency difference joint estimation module M4 is used to reconstruct the signal based on each IMF component after wavelet threshold processing, calculate the conjugate ambiguity function of the reconstructed received signal, and perform peak detection on the calculated conjugate ambiguity function to estimate the time delay difference and frequency difference between the multi-antenna signals of the distributed system.

[0144] It is not difficult to find that this embodiment is a system embodiment corresponding to the above method embodiment, and this embodiment can be implemented in conjunction with the above method embodiment. The relevant technical details and technical effects mentioned in the above embodiments are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiments.

[0145] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0146] Another embodiment of the present application provides an electronic device. The specific structure of the electronic device can be as follows: Figure 5 As shown, it includes: at least one processor C1; and a memory C2 that is communicatively connected to the at least one processor C1; wherein the memory C2 stores instructions that can be executed by the at least one processor C1, and the instructions are executed by the at least one processor C1 so that the at least one processor C1 can execute a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system as described in the above-mentioned method embodiments.

[0147] Among them, the memory and the processor are connected in a bus manner, and the bus includes any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripheral devices, voltage regulators and power management circuits together, which are all well known in the art, so this article will not describe them further. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna, and further, the antenna also receives the data and transmits the data to the processor.

[0148] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0149] Another embodiment of the present application proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system as described in the above method embodiments.

[0150] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (such as a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. Storage media include: U disk, mobile hard disk, read-only memory, random access memory, disk or optical disk and other media that can store program codes.

[0151] It can be understood by those skilled in the art that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present application. For those skilled in the art, several improvements and modifications can be made without departing from the principles of the present application, and these improvements and modifications are also considered to be within the scope of protection of the present application.

Claims

1. A method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system, characterized in that: The method comprises: The QPSO algorithm is used to optimize the variational mode decomposition algorithm to obtain the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor. Based on the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor, the received signal is subjected to variational mode decomposition to obtain multiple IMF components. Calculate the variance contribution rate of each IMF component respectively, and filter out the IMF components whose variance contribution rate is less than a first preset threshold; Perform wavelet threshold processing on each retained IMF component; Signal reconstruction is performed based on each IMF component after wavelet threshold processing, the conjugate ambiguity function of the reconstructed received signal is calculated, and the peak value of the conjugate ambiguity function is detected to estimate the time delay difference and frequency difference between multi-antenna signals in the distributed system.

2. The method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system according to claim 1, characterized in that: Before using the QPSO algorithm to optimize and solve the variational mode decomposition algorithm, the method further includes: Two received signals are obtained and a signal model is established for the two received signals. There is both a delay difference and a frequency difference between the two received signals. The established signal model is expressed by the formula: Wherein, x1(t) represents the first received signal, x2(t) represents the second received signal, a1 represents the amplitude attenuation of the first received signal during the propagation process, a2 represents the amplitude attenuation of the second received signal during the propagation process, s(t) represents the useful signal radiated by the radiation source, τ1 represents the time delay of the first received signal transmission, τ2 represents the time delay of the second received signal transmission, n1(t) represents the additive Gaussian noise added to the first received signal, and n2(t) represents the additive Gaussian noise added to the second received signal; The first received signal x1(t) is used as the reference signal to simplify the signal model. The simplified signal model is expressed by the formula: A=a1 / a2; D = τ2-τ1; Wherein, A is the attenuation coefficient ratio, D is the time delay difference between the two received signals to be determined, and Δf is the frequency difference between the two received signals to be determined.

3. The method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system according to claim 2, characterized in that: After simplifying the signal model, the method further includes: The fitness function is designed as the ratio of the envelope entropy and the kurtosis value; The calculation formula of envelope entropy is: Among them, E p represents the envelope entropy, K is the total number of IMF components obtained by variational mode decomposition, and a(k) represents the envelope signal of the kth IMF component obtained by variational mode decomposition after Hilbert demodulation; The calculation formula of kurtosis value is: Among them, K4 represents the kurtosis value, n represents the total number of discrete points in the discrete sequence, and x t It represents the vibration amplitude corresponding to the tth discrete point in the discrete sequence of the time domain waveform, and μ represents the average amplitude of the discrete sequence.

4. The method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system according to claim 3, characterized in that: The QPSO algorithm is used to optimize and solve the variational mode decomposition algorithm to obtain the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor. Based on the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor, the received signal is subjected to variational mode decomposition to obtain multiple IMF components, which is achieved by the following steps: S11, taking the decomposition number K and the penalty factor α as optimization variables, and setting the search range of the decomposition number K and the search range of the penalty factor α; S12, set the population size M and the spatial search dimension R, initialize the population particles, and set the number of iterations at this time to t = 1; S13, based on the designed fitness function, calculate the current fitness value of the particle. If the current fitness value is better than the fitness value of the previous iteration, the current position of the particle is used as the individual optimal position of the particle. Otherwise, the individual optimal position of the particle is not updated. The update of the individual optimal position is achieved by the following formula: in, is a random number between [0, 1] and follows a normal distribution. represents the individual optimal position of the i-th particle after updating at the t-th iteration, p id (t) represents the individual optimal position of the i-th particle before updating at the t-th iteration, p gd (t) represents the global optimal position; S14, comparing the fitness values ​​of all particles in the population with the global optimal position respectively, if the fitness value of a particle is better than the fitness value of the global optimal position, then the individual optimal position of the example whose fitness value is better than the fitness value of the global optimal position is taken as the global optimal position; S15, calculating the average optimal position of the particle swarm, the average optimal position of the particle swarm is calculated by the following formula: in, represents the average optimal position of the particle swarm; S16, calculating the potential well center of the non-optimal particle for each dimension of the particle, and calculating the position of a random point; S17, updating the particle position, calculating the new position of the particle in each dimensional space, the new position of the particle in each dimensional space is calculated by the following formula; Among them, β is the shrinkage expansion coefficient, η and ζ are the shrinkage expansion coefficient parameters, t max is the maximum number of iterations, X id (t) represents the new position of the i-th particle at the completion of the t-1th iteration, X id (t+1) represents the new position of the i-th particle at the completion of the t-th iteration; S18, update the number of iterations plus one, repeat S53 to S57, if the fitness decreases, update the number of decomposition layers K and the penalty factor α until the maximum number of iterations t is reached max , output the tth max The global optimal value obtained after iterations is used as the optimal solution for the number of decomposition layers K and the optimal solution for the penalty factor α; S19, performing variational mode decomposition on the received signal based on the optimal solution of the decomposition layer number K and the optimal solution of the penalty factor α to obtain multiple IMF components.

5. The method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system according to claim 4, characterized in that: The variance contribution rate of each IMF component is calculated separately, which is achieved through the following formula: Where K represents the total number of IMF components obtained by variational mode decomposition of the received signal, u k (t) represents the kth IMF component, VCR k Represents the variance contribution rate of the kth IMF component.

6. The method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system according to claim 5, characterized in that: Perform wavelet threshold processing on each retained IMF component, including: Select appropriate wavelet basis to perform layer wavelet decomposition on each IMF component; Perform adaptive wavelet threshold function processing at each scale to estimate the wavelet coefficients; The estimated wavelet coefficients are reconstructed and denoised to obtain the IMF components after wavelet threshold processing, that is, the denoised IMF components; The expression of the adaptive wavelet threshold function is as follows: in, represents the wavelet threshold function, λ represents the adaptive threshold, σ represents the noise variance, N represents the signal length, λ j represents the wavelet decomposition threshold of the jth layer, r 1,2j represents the correlation strength between the j-th layer wavelet decomposition coefficient and the original signal coefficient, a1 represents the original signal data, a 2,j represents the signal data after the j-th layer wavelet decomposition, Cov(·,·) represents the calculated covariance, Indicates the calculation of variance.

7. The method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system according to claim 6, characterized in that: The conjugate ambiguity function of the reconstructed received signal is calculated, and the peak value of the conjugate ambiguity function is detected to estimate the delay difference and frequency difference between the multi-antenna signals in the distributed system, which is achieved by the following formula: in, represents the reconstructed first received signal, represents the reconstructed second received signal, represents the conjugate ambiguity function of the reconstructed two-path received signals, and (D, Δf) represents the estimated time delay difference and frequency difference between the multi-antenna signals of the distributed system.

8. A system for jointly estimating time and frequency differences between multi-antenna signals in a distributed system, characterized in that: The system comprises: An IMF component acquisition module is used to optimize and solve the variational mode decomposition algorithm using the QPSO algorithm to obtain the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor, and perform variational mode decomposition on the received signal based on the optimal solution of the number of decomposition layers and the optimal solution of the penalty factor to obtain multiple IMF components; An IMF component filtering module, used to calculate the variance contribution rate of each IMF component respectively, and filter out the IMF component whose variance contribution rate is less than a first preset threshold; A wavelet threshold processing module is used to perform wavelet threshold processing on each retained IMF component; The time difference and frequency difference joint estimation module is used to reconstruct the signal based on each IMF component after wavelet threshold processing, calculate the conjugate ambiguity function of the reconstructed received signal, and perform peak detection on the calculated conjugate ambiguity function to estimate the time delay difference and frequency difference between the multi-antenna signals of the distributed system.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it can implement a method for jointly estimating time and frequency differences between multi-antenna signals in a distributed system as described in any one of claims 1 to 7.

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