Signal parameter determination method, device and computer equipment

Abstract

The present invention relates to the field of wireless communication technology, and discloses a method, device and computer equipment for determining signal parameters. The method comprises: obtaining a first signal to be processed currently; performing a sampling process on the first signal for a preset number of times to obtain a second signal corresponding to the first signal; performing data processing on the first signal and the second signal to obtain a first probability distribution from the first signal to the second signal, and a second probability distribution from the second signal to the first signal; based on the first probability distribution and the second probability distribution, updating the signal parameters of the first signal to obtain target signal parameters. By implementing the technical solution of the present invention, it is possible to update the signal parameters based on the probability distribution, thereby improving the signal processing accuracy and the performance of the wireless communication system.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and device for determining signal parameters and a computer device. Background Art

[0002] At present, the methods for determining wireless communication signal parameters mainly include maximum likelihood estimation (MLE), lp norm estimation and M estimator, etc. However, due to the actual application scenarios and abnormal points in the signal, traditional algorithms are often difficult to cope with, which may lead to falling into the local optimal solution or producing inaccurate estimation results. Summary of the invention

[0003] In view of this, the present invention provides a method, apparatus and computer device for determining signal parameters to solve the problems of local optimal solution and inaccurate estimation caused by abnormal points and complex scenarios in practical applications of traditional wireless communication signal parameter determination methods.

[0004] In a first aspect, the present invention provides a method for determining signal parameters, comprising: obtaining a first signal to be processed; performing sampling processing on the first signal a preset number of times to obtain a second signal corresponding to the first signal; performing data processing on the first signal and the second signal to obtain a first probability distribution from the first signal to the second signal, and a second probability distribution from the second signal to the first signal; based on the first probability distribution and the second probability distribution, updating the signal parameters of the first signal to obtain target signal parameters.

[0005] The method for determining signal parameters provided by the embodiment of the present invention obtains the probability distribution between the first signal and the second signal by performing multiple sampling and data processing on the first signal, thereby updating the signal parameters. Therefore, the method can effectively improve the accuracy of signal parameter estimation, solve the problem that the traditional method may lead to local optimal solutions or inaccurate estimates in complex signal environments, and has strong robustness and adaptability.

[0006] In an optional embodiment, a first signal is sampled a preset number of times to obtain a second signal corresponding to the first signal, including: obtaining a first random number corresponding to the current sampling process; determining a processing mode of the first signal based on the first random number, and performing data processing on the first signal according to the processing mode to obtain a second signal.

[0007] The signal parameter determination method provided in the embodiment of the present invention can avoid the limitations brought by the fixed processing method by introducing randomness and dynamically selecting the processing mode, making the signal processing process more flexible and adaptable, thereby improving the diversity and accuracy of signal processing, and helping to cope with different signal characteristics and noise environments.

[0008] In an optional embodiment, based on the first probability distribution and the second probability distribution, the signal parameters of the first signal are updated to obtain the target signal parameters, including: determining the acceptance rate based on the first probability distribution and the second probability distribution; and updating the signal parameters of the first signal based on the acceptance rate to obtain the target signal parameters.

[0009] The signal parameter determination method provided in the embodiment of the present invention adopts an acceptance rate update mechanism to effectively improve the robustness of the parameter estimation process, avoid the local optimal solution problem that may occur in traditional methods, enhance the stability and accuracy of the estimation results, and have stronger adaptability.

[0010] In an optional embodiment, the acceptance rate is determined based on the first probability distribution and the second probability distribution, including: obtaining a first preset prior distribution of the first signal, and a second preset prior distribution of the second signal; performing data processing on the first preset prior distribution to obtain a first posterior distribution of the first signal; performing data processing on the second preset prior distribution to obtain a second posterior distribution of the second signal; and determining the acceptance rate based on the first posterior distribution, the second posterior distribution, the first probability distribution, and the second probability distribution.

[0011] The signal parameter determination method provided by the embodiment of the present invention can more accurately capture the real characteristics of the signal through the careful processing of the prior distribution and the posterior distribution, thereby improving the accuracy of determining the acceptance rate. Therefore, the method can effectively utilize the historical information and statistical characteristics of the signal, enhance the adaptability and robustness of parameter estimation in complex signal environments, improve the accuracy of the estimation results, and reduce errors caused by noise or abnormal points.

[0012] In an optional embodiment, a first correlation of multiple signal parameters in a first posterior distribution is identified; a first redundant parameter among the multiple signal parameters is determined based on the first correlation, and the first posterior distribution is simplified using the first redundant parameter; and / or, a second correlation of multiple signal parameters in a second posterior distribution is identified; a second redundant parameter among the multiple signal parameters is determined based on the second correlation, and the second posterior distribution is simplified using the second redundant parameter.

[0013] The signal parameter determination method provided by the embodiment of the present invention can improve the efficiency and accuracy of signal parameter estimation by optimizing the redundant parameters in the first posterior distribution and the second posterior distribution, avoid unnecessary redundant calculations, and enhance the real-time performance and robustness of the algorithm. In addition, the removal of redundant parameters helps to reduce the impact of noise, making the final signal parameter estimation result more stable and accurate.

[0014] In an optional embodiment, the acceptance rate is determined based on the first posterior distribution, the second posterior distribution, the first probability distribution, and the second probability distribution, including: determining a first ratio of the second posterior distribution to the first posterior distribution; determining a second ratio of the second probability distribution to the first probability distribution; and determining the acceptance rate based on the product of the first ratio and the second ratio.

[0015] The method for determining signal parameters provided by the embodiment of the present invention determines the acceptance rate by calculating the ratio of the second posterior distribution to the first posterior distribution, and the ratio of the second probability distribution to the first probability distribution, and combining the product of the two, thereby more accurately reflecting the relationship between the signal parameters. Therefore, the method can more effectively capture the uncertainty and change trend of the signal by using the ratio relationship, thereby optimizing the update process of the signal parameters and improving the accuracy and robustness of the estimation.

[0016] In an optional embodiment, the signal parameters of the first signal are updated based on the acceptance rate to obtain the target signal parameters, including: determining whether to use the second signal to update the first signal based on the acceptance rate; if the second signal is used to update the first signal, determining the signal parameters of the second signal as the target signal parameters.

[0017] The method for determining signal parameters provided by the embodiment of the present invention determines whether to use the second signal to update the signal parameters of the first signal based on the judgment mechanism of the acceptance rate, thereby enhancing the flexibility and adaptability of parameter updating. When the update of the second signal helps to improve the estimation accuracy of the first signal parameters, the parameters of the second signal can be automatically selected and utilized to ensure the accuracy of the target signal parameters. Therefore, the method can effectively cope with the complex correlation and noise interference between signals, avoid unnecessary updates, and improve the calculation efficiency at the same time, ensuring that the signal estimation result is more stable and more accurate.

[0018] In an optional implementation, determining whether to use the second signal to update the first signal based on the acceptance rate includes: if the acceptance rate is less than the second random number corresponding to the current sampling process, using the second signal to update the first signal.

[0019] The signal parameter determination method provided by the embodiment of the present invention realizes dynamic and probabilistic signal update decision by introducing a comparison mechanism of acceptance rate and random number, thereby enhancing the robustness and adaptability of the system.

[0020] In a second aspect, the present invention provides a device for determining signal parameters, comprising: an acquisition module for acquiring a first signal to be processed; a sampling module for performing sampling processing on the first signal for a preset number of times to obtain a second signal corresponding to the first signal; a data processing module for performing data processing on the first signal and the second signal to obtain a first probability distribution from the first signal to the second signal, and a second probability distribution from the second signal to the first signal; an updating module for updating the signal parameters of the first signal based on the first probability distribution and the second probability distribution to obtain target signal parameters.

[0021] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the signal parameter determination method of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for determining signal parameters of the first aspect or any corresponding embodiment thereof.

[0023] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for determining signal parameters of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 is a flow chart of a method for determining signal parameters according to an embodiment of the present invention;

[0026] Figure 2 is a flow chart of another method for determining signal parameters according to an embodiment of the present invention;

[0027] Figure 3 is a structural block diagram of a device for determining signal parameters according to an embodiment of the present invention;

[0028] Figure 4 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] In the related art, the parameter estimation methods of wireless communication signals include least squares (LS), maximum likelihood estimation (MLE), Lp norm estimation, M estimator and Markov chain Monte Carlo (MCMC) method, etc. Robust parameter estimation methods are usually expanded and optimized on the basis of these traditional methods to cope with complex practical application scenarios.

[0031] Parameter estimation is a key technology in signal processing and is widely used in wireless communications, radar communications, and other fields. However, with the diversification of application scenarios and the possibility of outliers in signals, traditional parameter estimation algorithms often find it difficult to provide accurate estimation results when faced with these changes, and may even fall into a local optimal solution, resulting in performance degradation.

[0032] Maximum likelihood estimation is based on maximizing the probability of observed data to derive signal distribution parameters, assuming that the signal distribution is known. However, MLE is prone to fall into local optimal solutions, resulting in inefficient parameter estimation. In contrast, MCMC can conduct a comprehensive search in parameter space to find the global optimal solution by constructing a Markov chain, which is particularly suitable for problems with multiple local optimal solutions.

[0033] Lp norm estimation is usually used in noisy environments to improve the estimation accuracy by minimizing the Lp norm of the residual between the signal and the model. However, the solution process of this method is relatively complicated and computationally intensive, making it unsuitable for real-time applications. In contrast, MCMC can efficiently approximate the probability distribution of parameters through random sampling, avoiding complex optimization processes.

[0034] The M-estimator estimates parameters by minimizing the negative value of the sample log-likelihood function and introduces the weight of data points to improve robustness. However, since the M-estimator is easily affected by the selection of initial values, it may fall into local extreme values, thus affecting the accuracy of the estimation results. The MCMC method can effectively handle more complex models and larger data sets, thus avoiding this problem.

[0035] In view of this, the technical solution of the present invention obtains a second signal by acquiring a first signal to be processed and sampling it multiple times. By processing the data of the first signal and the second signal, two probability distributions are obtained, namely, a first probability distribution from the first signal to the second signal and a second probability distribution from the second signal to the first signal. Based on these two probability distributions, the parameters of the first signal are updated to obtain the final parameters of the target signal, thereby improving the accuracy and reliability of signal processing.

[0036] According to an embodiment of the present invention, an embodiment of a method for determining signal parameters is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] In this embodiment, a method for determining a signal parameter is provided, which can be used in computer devices, such as desktop computers, notebook computers, etc. Figure 1 is a flow chart of a method for determining signal parameters according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0038] Step S101, obtaining a first signal to be processed currently.

[0039] The first signal refers to an original signal received from a wireless communication system, including Gaussian white noise and the superposition of multiple sinusoidal signals. Specifically, the first signal with noise can be received from a sensor, a wireless receiving device, and the like.

[0040] Among them, a signal model of the first signal can be established. In the presence of Gaussian white noise, the observed first signal y=[y0,y1,L,y N-1 ] T When the number of signal models k is 0 and the number of signal models k ≥ 1, the models are established respectively. When k = 0, the model only represents noise samples, and when k ≥ 1, it represents the superposition of k sinusoidal signals under noise corruption, which can be specifically expressed as:

[0041]

[0042] in are the amplitudes in different directions of the j-th sinusoidal signal with k signal models, is the radial frequency. For different sinusoidal signals j1≠j2, the corresponding frequency They are not equal. k represents a noise sequence with a mean of 0 and a variance of Gaussian noise. Then in the form of vector matrix, the observed data can be modeled as:

[0043] y=D(ω k ) k +n k (Formula 2)

[0044] where a k is a 2k×1 vector matrix, and Represents the amplitude matrix a k The odd-numbered rows of Represents the amplitude matrix a k Even rows; D(ω k ) is an N×2k frequency vector matrix, defined as:

[0045]

[0046] For i = 0, L, N-1 and j = 1, L, k, [D(ω k )] i+1,2j-1 and [D(ω k )] i+1,2j Represents the odd and even columns of the matrix respectively. n k represents a noise sequence with a mean of 0 and a variance of Gaussian noise can be expressed as:

[0047]

[0048] Step S102: Sampling the first signal a preset number of times to obtain a second signal corresponding to the first signal.

[0049] The second signal is a new signal sample obtained by sampling the first signal. Specifically, by sampling the first signal a certain number of times, multiple instances (second signals) of the first signal under different frequency, amplitude and noise variance conditions can be obtained. These sampled data help capture the diversity and uncertainty of the first signal.

[0050] Step S103: performing data processing on the first signal and the second signal to obtain a first probability distribution from the first signal to the second signal, and a second probability distribution from the second signal to the first signal.

[0051] The first probability distribution refers to the probability distribution of the occurrence of the second signal given the first signal during the sampling process. It can also be understood that the first probability distribution is used to characterize the possibility or distribution form of the second signal obtained from the first signal through a preset sampling process. Specifically, by sampling the first signal a preset number of times, a plurality of possible second signal instances are generated, and the frequency or distribution of the occurrence of these second signals is counted to obtain the probability distribution of the first signal to the second signal. For example, statistical methods such as maximum likelihood estimation and Bayesian inference can be used to fit this probability distribution.

[0052] The second probability distribution refers to the probability distribution of the first signal appearing in the sampling process given the second signal. The second probability distribution describes the reverse sampling process, that is, the possibility of inferring the first signal from the second signal. Specifically, in a similar manner, reverse sampling processing is performed on the second signal to generate multiple groups of possible first signals. Based on these sampling results, the probability distribution of the second signal to the first signal is obtained.

[0053] Step S104: Based on the first probability distribution and the second probability distribution, update the signal parameters of the first signal to obtain target signal parameters.

[0054] The target signal parameters refer to a set of parameters obtained through the updating process that can more accurately describe the characteristics of the first signal, such as the number of signals in the first signal, the frequency of each signal, etc. Specifically, by comparing and calculating the first probability distribution and the second probability distribution, the parameters of the first signal can be corrected to more accurately describe the relationship between the signals, thereby obtaining accurate target signal parameters.

[0055] The method for determining signal parameters provided by the embodiment of the present invention obtains the probability distribution between the first signal and the second signal by performing multiple sampling and data processing on the first signal, thereby updating the signal parameters. Therefore, the method can effectively improve the accuracy of signal parameter estimation, solve the problem that the traditional method may lead to local optimal solutions or inaccurate estimates in complex signal environments, and has strong robustness and adaptability.

[0056] In this embodiment, a method for determining a signal parameter is provided, which can be used in computer devices, such as desktop computers, notebook computers, etc. Figure 2 is a flow chart of a method for determining signal parameters according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0057] Step S201, obtain the first signal to be processed. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0058] Step S202: Sampling the first signal a preset number of times to obtain a second signal corresponding to the first signal.

[0059] Specifically, the above step S202 includes:

[0060] Step S2021, obtaining a first random number corresponding to the current sampling process.

[0061] The first random number (u) refers to a value randomly generated from a uniform distribution U(0, 1). Specifically, in the RJMCMC (Reversible Jump Markov Chain Monte Carlo) algorithm, u affects whether to jump between different moving processes (such as birth, death, merge, split or update). For example, the generated u can be used to determine whether to add or delete the number of signal models k in the iteration, or whether to update certain parameters (such as the frequency ω k ).

[0062] Step S2022: determine a processing mode of the first signal based on the first random number, and perform data processing on the first signal according to the processing mode to obtain a second signal.

[0063] In the RJMCMC algorithm, there are five processing modes: birth, death, split, merge, and update, which correspond to model parameters (such as the number of signals k and the frequency ω k ) operations. Each processing mode has a corresponding probability and acceptance condition, which is determined by u. Specifically, when the number of signals is unknown, the RJMCMC algorithm k value and ω k To accurately describe the jump of the RJMCMC method in different dimensional parameter spaces, the iterative parameter update process is divided into five moving processes, namely birth, death, merge, split, and update, to achieve the search and estimation of the parameter space. In each iteration, according to a specific probability b k , d k and u k Jump between several processes and satisfy:

[0064] 2b k +2d k +u k =1(Formula 5)

[0065] Among them, b k and d k It can be defined as:

[0066]

[0067] Among them, c is the parameter for adjusting the jump speed, p(k) is the probability density function of the number of models k, which obeys the truncated Poisson distribution, then the model prior probability p(k) can be expressed as:

[0068]

[0069] Here, λ is the scale parameter of the truncated Poisson distribution, which is set to the number of signal models and varies with the number of iterations, expressed as different values ​​of k.

[0070] According to formula (6), we can obtain the b of the first signal in this iteration: k(i) and d k(i) , and compare it with u. Specifically, if u≤b k(i) , select the birth process; if u≤b k(i) +d k(i) , select the death process; if u≤2b k(i) +d k(i) , select the split process; if u≤2(b k(i) +d k(i) ), select the merge process; otherwise select the Update process. After the first signal is processed according to any processing mode, a second signal is obtained.

[0071] The signal parameter determination method provided in the embodiment of the present invention can avoid the limitations brought by the fixed processing method by introducing randomness and dynamically selecting the processing mode, making the signal processing process more flexible and adaptable, thereby improving the diversity and accuracy of signal processing, and helping to cope with different signal characteristics and noise environments.

[0072] Step S203: performing data processing on the first signal and the second signal to obtain a first probability distribution from the first signal to the second signal, and a second probability distribution from the second signal to the first signal.

[0073] For the forward process (from the first signal to the second signal), calculate the acceptance candidate state x * (parameter state of the second signal); and for the reverse process (from the second signal to the first signal), the probability of transferring back to the original state is calculated. Specifically, when the parameter state of the first signal is x l , the parameter state of the second signal is x * When calculating from the current state x l To candidate state x * The proposed distribution q(x * ∣x l ), i.e. the first probability distribution; and calculate from the candidate state x * To the current state xl The proposed distribution q(x l ∣x * ), which is the second probability distribution.

[0074] Step S204: Based on the first probability distribution and the second probability distribution, update the signal parameters of the first signal to obtain target signal parameters.

[0075] Specifically, the above step S204 includes:

[0076] Step S2041, determining the acceptance rate based on the first probability distribution and the second probability distribution.

[0077] The acceptance rate r refers to the decision of whether to accept the parameter state x from the current state (the first signal) in the Markov Chain Monte Carlo (MCMC) method. l ) to the candidate state (parameter state x of the second signal * Specifically, the acceptance rate is calculated based on the reverse and forward probabilities (first probability distribution and second probability distribution) of the proposed distribution.

[0078] In some optional implementations, the above step S2041 includes:

[0079] Step a1: acquiring a first preset prior distribution of the first signal and a second preset prior distribution of the second signal.

[0080] The first preset prior distribution and the second preset prior distribution refer to the initial assumption distribution of the parameters (or states) of the first signal and the second signal in the absence of observed data. Specifically, according to the Bayesian distribution, the number k of sinusoidal signals in the assumed signal and the parameter is unknown and satisfies θ k , the priors for k and y are statistically IID. The noise parameters It obeys the conjugate inverse gamma prior distribution. Assuming that it obeys Jeffreys's uninformative prior, then The number of models k follows a truncated Poisson distribution.

[0081] Step a2: performing data processing on the first preset prior distribution to obtain a first posterior distribution of the first signal.

[0082] Step a3: performing data processing on the second preset prior distribution to obtain a second posterior distribution of the second signal.

[0083] First posterior distribution is based on the first preset prior distribution p(x l ) and the likelihood function of the first signal p(y|x l) is calculated to characterize the parameter distribution of the updated first signal. The second posterior distribution is based on the second preset prior distribution p(x * ) and the likelihood function of the second signal p(y|x * ) is calculated to characterize the parameter distribution of the updated second signal. Specifically, data processing can be performed according to the Bayesian formula to obtain the posterior distribution of the signal. For example, when the number of signal models in the first signal is known, the frequency ω k Obey U k The uniform distribution on , then the posterior distribution expression of the unknown parameters in the signal model is:

[0084]

[0085] Step a4, identifying a first correlation among multiple signal parameters in a first posterior distribution; determining a first redundant parameter among the multiple signal parameters based on the first correlation, and simplifying the first posterior distribution using the first redundant parameter; and / or, identifying a second correlation among multiple signal parameters in a second posterior distribution; determining a second redundant parameter among the multiple signal parameters based on the second correlation, and simplifying the second posterior distribution using the second redundant parameter.

[0086] The first correlation of multiple signal parameters in the first posterior distribution and the second correlation of multiple signal parameters in the second posterior distribution are identified by calculating the covariance or correlation coefficient between each signal parameter. Specifically, the parameter pairs that are highly correlated in the first posterior distribution are identified by statistical analysis (such as Pearson correlation coefficient, covariance matrix and other methods), and these highly correlated parameters indicate that they provide redundant information. Based on the identified correlation, the redundant parameters can be determined by selecting one of the parameters to represent the parameter group with strong correlation. For example, if a high correlation is found between multiple parameters in the first posterior distribution, a key parameter (redundant parameter) can be selected as a representative, and other redundant parameters can be removed. In this way, the first posterior distribution can be simplified, and unnecessary dimensions and calculations can be reduced.

[0087] Similarly, for the second posterior distribution, a similar method is used to calculate the correlation between parameters, identify highly correlated parameter pairs, thereby determining redundant parameters and simplifying the second posterior distribution. The simplified posterior distribution helps to update parameters more efficiently and reduce the complexity of calculation. For example, due to the amplitude a and the noise variance σ 2 is a redundant parameter, so it is necessary to simplify the posterior distribution expression (8) by analytical parameter integration. It is known that the amplitude follows a normal distribution and the noise follows an inverse gamma distribution, so the posterior distribution expression after simplification according to the Bayesian method is:

[0088]

[0089] Step a5, determining the acceptance rate based on the first posterior distribution, the second posterior distribution, the first probability distribution and the second probability distribution.

[0090] Calculate the posterior distribution of the current state and the candidate state, and then combine the proposed distribution from the current state to the candidate state and from the candidate state to the current state, and calculate the acceptance rate according to a certain formula.

[0091] In the above implementation, by carefully processing the prior distribution and the posterior distribution, the real characteristics of the signal can be captured more accurately, thereby improving the accuracy of determining the acceptance rate. Therefore, this method can effectively utilize the historical information and statistical characteristics of the signal, enhance the adaptability and robustness of parameter estimation in complex signal environments, improve the accuracy of the estimation results, and reduce errors caused by noise or outliers.

[0092] In some optional implementations, the above step a5 includes:

[0093] Step a51, determining a first ratio of the second posterior distribution to the first posterior distribution.

[0094] Step a52: determining a second ratio of the second probability distribution to the first probability distribution.

[0095] Step a53, determining the acceptance rate based on the product of the first ratio and the second ratio.

[0096] Calculate the first ratio of the second posterior distribution to the first posterior distribution, and the second ratio of the second probability distribution to the first probability distribution. According to the Metropolis-Hastings algorithm, the calculation formula for the acceptance rate r is:

[0097]

[0098] Specifically, when entering a specific movement process, a specific movement probability will be used to choose whether to accept the movement operation. The acceptance probability of birth movement is α birth for:

[0099] α birth =min{1,r birth}(Formula 11)

[0100] Among them, r birth is the acceptance rate of the birth move proposal. Similarly, the acceptance rates of death, merge, and split moves are:

[0101]

[0102] αsplit =min{1,r split}(Formula 13)

[0103] α merge =min{1,r merge}(Formula 14)

[0104] The acceptance ratio of movement under specific models is different, and combined with the posterior distribution expression, we can get:

[0105]

[0106] Among them, ζ, υ, ψ represent scale parameters, δ 2 Represents the signal-to-noise ratio.

[0107] Among them, if u≤b k(i) When selecting the birth process, a new frequency is first randomly generated. At the same time, update D(ω k ) and the correlation matrix; secondly, according to formula (11) and formula (15), we can obtain α birth , randomly obtain a sample Then if u≤α birth , the parameters become Otherwise it remains To achieve the purpose of parameter update.

[0108] If u≤b k(i) +d k(i) When selecting the death process, first select the existing ω k Select a value to delete and update D(ω k ) and the correlation matrix; secondly, according to formula (12) and formula (16), we can obtain α death , randomly obtain a sample Then if u≤α death , the parameters become Otherwise it remains To achieve the purpose of parameter update.

[0109] If u≤2b k(i) +d k(i) When selecting the split process, first select the existing ω k Randomly select a value ω i Divide into two, update the frequency vector matrix D(ω k ) and the correlation matrix; secondly, according to formula (13) and formula (17), we can obtain α split , randomly obtain a sample Then if u≤α split , the parameters become Otherwise it remains To achieve the purpose of parameter update.

[0110] If u≤2(b k(i) +d k(i) ), when selecting the merge process, first k Find two equal ω i ω j , and get the average value ω t , update D(ω k ) and the correlation matrix; secondly, according to formula (14) and formula (18), we can obtain α merge , randomly obtain a sample Then if u≤α merge , the parameters become Otherwise it remains To achieve the purpose of parameter update.

[0111] In other cases, select the Update process, keep the k value unchanged, and update the frequency parameter.

[0112] Furthermore, an inner loop is used in this process, that is, this is a loop nested execution process, and finally the estimated value of the parameter is obtained.

[0113] In the above implementation, the acceptance rate is determined by calculating the ratio of the second posterior distribution to the first posterior distribution, and the ratio of the second probability distribution to the first probability distribution, and combining the product of the two, so as to more accurately reflect the relationship between the signal parameters. Therefore, this method can more effectively capture the uncertainty and change trend of the signal by using the ratio relationship, thereby optimizing the update process of the signal parameters and improving the accuracy and robustness of the estimation.

[0114] Step S2042: Update the signal parameters of the first signal based on the acceptance rate to obtain target signal parameters.

[0115] Calculate the acceptance rate. If the generated random number is less than or equal to the acceptance rate, accept the candidate state and update the current signal parameters to the signal parameters of the candidate state, that is, the target signal parameters are the signal parameters of the candidate state; otherwise, keep the current state unchanged, that is, the target signal parameters are still the signal parameters of the current state.

[0116] In some optional implementations, the above step S2042 includes:

[0117] Step b1, determining whether to update the first signal with the second signal based on the acceptance rate.

[0118] If the acceptance rate is greater than a preset threshold (for example, it can be calculated by the Metropolis-Hastings algorithm or a similar method), it means that the parameters of the second signal can effectively improve or update the parameters of the first signal, thereby deciding whether to accept the update of the second signal.

[0119] In some optional implementations, the above step b1 includes: if the acceptance rate is less than the second random number corresponding to the current sampling process, using the second signal to update the first signal.

[0120] The second random number is a value generated from a uniform distribution U(0,1), representing a random decision process used to decide whether to update the signal. Specifically, if the acceptance rate r is less than the second random number, then update. This means that the current state is rejected and the candidate state (second signal) will become the new state. If the acceptance rate is greater than or equal to the second random number, then do not update and keep the current signal (first signal) unchanged.

[0121] Step b2: if the second signal is used to update the first signal, the signal parameters of the second signal are determined as the target signal parameters.

[0122] If the second signal is used to update the first signal, the signal parameters of the second signal are used as the new target signal parameters, which means that the second signal effectively replaces the first signal under certain conditions. Specifically, for the lth iteration, l = L + 1, ..., M, the covariance matrix is ​​Σ (l) , and then continue to iterate until the predetermined total number of iterations is reached or the experimental results converge to the true value. The sample sequence ω is an M×k matrix. Then the sampling results of the burn-in cycle L times are discarded, that is, the first L rows of data are discarded, and then the remaining matrix is ​​averaged by row, so as to obtain the i-th result of the outer cycle. The ωk obtained each time in the inner layer is taken as the i-th column of ω, and then the average is calculated by column. ω is a k max ×M matrix, and after averaging, a 1×M matrix is ​​obtained. The sampling results of the burn-in period L are discarded, and the inner and outer loops are completed. After the above operations, the Markov chain gradually converges, and the number k and frequency ω of the signal model finally converge to the true value, and the target signal parameters are obtained.

[0123] The method for determining signal parameters provided by the embodiment of the present invention determines whether to use the second signal to update the signal parameters of the first signal based on the judgment mechanism of the acceptance rate, thereby enhancing the flexibility and adaptability of parameter updating. When the update of the second signal helps to improve the estimation accuracy of the first signal parameters, the parameters of the second signal can be automatically selected and utilized to ensure the accuracy of the target signal parameters. Therefore, the method can effectively cope with the complex correlation and noise interference between signals, avoid unnecessary updates, and improve the calculation efficiency at the same time, ensuring that the signal estimation result is more stable and more accurate.

[0124] In the present embodiment, a device for determining signal parameters is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions thereof will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and contemplated.

[0125] This embodiment provides a device for determining signal parameters, such as Figure 3 As shown, including:

[0126] An acquisition module 301 is used to acquire a first signal to be processed currently;

[0127] The sampling module 302 is used to perform a preset number of sampling processes on the first signal to obtain a second signal corresponding to the first signal;

[0128] A data processing module 303 is used to perform data processing on the first signal and the second signal to obtain a first probability distribution from the first signal to the second signal and a second probability distribution from the second signal to the first signal;

[0129] The updating module 304 is configured to update the signal parameters of the first signal based on the first probability distribution and the second probability distribution to obtain target signal parameters.

[0130] In some optional implementations, the sampling module 302 includes:

[0131] An acquisition submodule, used to acquire a first random number corresponding to the current sampling process;

[0132] The processing submodule is used to determine a processing mode of the first signal based on the first random number, and perform data processing on the first signal according to the processing mode to obtain a second signal.

[0133] In some optional implementations, the updating module 304 includes:

[0134] A determination submodule, configured to determine an acceptance rate based on the first probability distribution and the second probability distribution;

[0135] The updating submodule is used to update the signal parameters of the first signal based on the acceptance rate to obtain the target signal parameters.

[0136] In some optional implementations, the determining submodule includes:

[0137] An acquisition unit, configured to acquire a first preset prior distribution of the first signal and a second preset prior distribution of the second signal;

[0138] A first processing unit, configured to perform data processing on a first preset prior distribution to obtain a first posterior distribution of a first signal;

[0139] A second processing unit, configured to perform data processing on a second preset prior distribution to obtain a second posterior distribution of a second signal;

[0140] The first determining unit is used to determine the acceptance rate based on the first posterior distribution, the second posterior distribution, the first probability distribution, and the second probability distribution.

[0141] In some optional implementations, the determining submodule further includes:

[0142] A simplification unit is used to identify a first correlation of multiple signal parameters in a first posterior distribution; determine a first redundant parameter among the multiple signal parameters based on the first correlation, and simplify the first posterior distribution using the first redundant parameter; and / or, identify a second correlation of multiple signal parameters in a second posterior distribution; determine a second redundant parameter among the multiple signal parameters based on the second correlation, and simplify the second posterior distribution using the second redundant parameter.

[0143] In some optional implementations, the first determining unit includes:

[0144] A first determining subunit, configured to determine a first ratio of the second posterior distribution to the first posterior distribution;

[0145] A second determining subunit, configured to determine a second ratio of the second probability distribution to the first probability distribution;

[0146] The third determining subunit is configured to determine an acceptance rate based on a product of the first ratio and the second ratio.

[0147] In some optional implementations, the updating submodule includes:

[0148] an updating unit, configured to determine whether to update the first signal using the second signal based on the acceptance rate;

[0149] The second determining unit is configured to determine the signal parameter of the second signal as the target signal parameter if the second signal is used to update the first signal.

[0150] In some optional implementations, the updating unit includes:

[0151] The updating subunit is used to update the first signal using the second signal if the acceptance rate is less than the second random number corresponding to the current sampling process.

[0152] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0153] The signal parameter determination device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0154] The signal parameter determination device provided in the embodiment of the present invention obtains the probability distribution between the first signal and the second signal by performing multiple sampling and data processing on the first signal, thereby updating the signal parameters. Therefore, the method can effectively improve the accuracy of signal parameter estimation, solve the problem that the traditional method may lead to local optimal solutions or inaccurate estimates in complex signal environments, and has strong robustness and adaptability.

[0155] The embodiment of the present invention also provides a computer device having the above Figure 3 The device for determining the signal parameters shown.

[0156] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0157] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0158] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0159] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0160] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0161] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0162] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0163] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0164] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0165] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for determining a signal parameter, characterized in that: The method comprises: Get the first signal currently to be processed; Performing sampling processing on the first signal for a preset number of times to obtain a second signal corresponding to the first signal; Performing data processing on the first signal and the second signal to obtain a first probability distribution from the first signal to the second signal, and a second probability distribution from the second signal to the first signal; Based on the first probability distribution and the second probability distribution, signal parameters of the first signal are updated to obtain target signal parameters.

2. The method according to claim 1, characterized in that The sampling process of the first signal for a preset number of times to obtain a second signal corresponding to the first signal includes: Obtaining a first random number corresponding to the current sampling process; A processing mode of the first signal is determined based on the first random number, and data processing is performed on the first signal according to the processing mode to obtain the second signal.

3. The method according to claim 1 or 2, characterized in that: The updating, based on the first probability distribution and the second probability distribution, of the signal parameters of the first signal to obtain target signal parameters includes: determining an acceptance rate based on the first probability distribution and the second probability distribution; The signal parameters of the first signal are updated based on the acceptance rate to obtain target signal parameters.

4. The method according to claim 3, characterized in that The determining of the acceptance rate based on the first probability distribution and the second probability distribution comprises: Acquire a first preset prior distribution of the first signal and a second preset prior distribution of the second signal; Performing data processing on the first preset prior distribution to obtain a first posterior distribution of the first signal; Performing data processing on the second preset prior distribution to obtain a second posterior distribution of the second signal; The acceptance rate is determined based on the first posterior distribution, the second posterior distribution, the first probability distribution, and the second probability distribution.

5. The method according to claim 4, characterized in that Also includes: identifying a first correlation of a plurality of signal parameters in the first posterior distribution; Determine a first redundant parameter among the plurality of signal parameters based on the first correlation, and simplify the first posterior distribution using the first redundant parameter; and / or, identifying a second correlation of a plurality of signal parameters in the second posterior distribution; A second redundant parameter among the plurality of signal parameters is determined based on the second correlation, and the second posterior distribution is simplified using the second redundant parameter.

6. The method according to claim 4, characterized in that The determining the acceptance rate based on the first posterior distribution, the second posterior distribution, the first probability distribution, and the second probability distribution comprises: determining a first ratio of the second posterior distribution to the first posterior distribution; determining a second ratio of the second probability distribution to the first probability distribution; The acceptance rate is determined based on a product of the first ratio and the second ratio.

7. The method according to claim 3, characterized in that The updating of the signal parameter of the first signal based on the acceptance rate to obtain the target signal parameter includes: determining whether to update the first signal with a second signal based on the acceptance rate; If the first signal is updated by using a second signal, the signal parameter of the second signal is determined as the target signal parameter.

8. The method according to claim 7, characterized in that The determining whether to update the first signal with a second signal based on the acceptance rate comprises: If the acceptance rate is less than the second random number corresponding to the current sampling process, the first signal is updated using the second signal.

9. A device for determining a signal parameter, characterized in that: The device comprises: An acquisition module, used for acquiring a first signal to be processed currently; A sampling module, used for performing a preset number of sampling processes on the first signal to obtain a second signal corresponding to the first signal; a data processing module, configured to perform data processing on the first signal and the second signal to obtain a first probability distribution from the first signal to the second signal, and a second probability distribution from the second signal to the first signal; An updating module is used to update the signal parameters of the first signal based on the first probability distribution and the second probability distribution to obtain target signal parameters.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for determining the signal parameters according to any one of claims 1 to 8 by executing the computer instructions.

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