Radar signal recovery method and device and electronic equipment

Abstract

The invention is suitable for the technical field of signal processing, and provides a radar signal recovery method and device and electronic equipment, and the method comprises the steps: receiving continuous radar signals, and obtaining a first radar signal sequence; according to the first radar signal sequence and a preset posterior probability model, the marginal probability of a second radar signal sequence is determined, the posterior probability model is constructed according to physical characteristics of radar signals, and the second radar signal sequence is a signal sequence obtained by recovering the first radar signal sequence; and determining a mean value of the marginal probabilities to obtain the second radar signal sequence. Through the above method, the accuracy of the recovered radar signal can be improved.

Description

Technical Field

[0001] This application belongs to the technical field of signal processing, and particularly relates to a radar signal recovery method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] A radar detects a target by transmitting an electromagnetic signal. Specifically, during the propagation of the electromagnetic signal, if it encounters a target, reflection will occur. After the radar receives the reflected signal (or echo signal), the presence of the target can be determined based on information such as the return time and intensity of the reflected signal. For example, in the field of hydrological monitoring, frequency-modulated continuous waves are usually used to detect targets. However, due to the weak reflection ability of the target, or the long distance between the radar and the target, or interference from the external environment, it is still impossible to effectively detect the target after receiving the reflected signal.

[0003] In existing methods, the reflected signal received by the radar is usually simply digitally filtered, such as digital windowing to recover the reflected signal. However, such traditional filtering schemes are all based on the maximum likelihood estimation algorithm and cannot theoretically recover the best performance of the signal, and the resulting signal-to-noise ratio quality is still poor.

[0004] Therefore, a new method is needed to recover the radar reflected signal. Summary of the Invention

[0005] Embodiments of this application provide a radar signal recovery method, device, and electronic device, which can solve the problem of poor accuracy of the radar signal recovered by existing methods.

[0006] In a first aspect, embodiments of this application provide a radar signal recovery method, including:

[0007] Receiving continuous radar signals to obtain a first radar signal sequence;

[0008] Determining the marginal probability of a second radar signal sequence according to the first radar signal sequence and a preset posterior probability model, where the posterior probability model is constructed based on the physical characteristics of the radar signal, and the second radar signal sequence is: the signal sequence obtained by recovering the first radar signal sequence;

[0009] Determining the mean value of the marginal probability to obtain the second radar signal sequence.

[0010] The beneficial effects of the embodiments of this application compared with the prior art are:

[0011] In the embodiments of the present application, since the mean value of the marginal probability of the second radar signal sequence can be obtained by integrating the second radar signal sequence and the posterior probability of the second radar signal sequence, and after integrating the second radar signal sequence and the posterior probability of the second radar signal sequence, the obtained value is approximate to the second radar signal sequence. Therefore, determining the mean value of the marginal probability of the second radar signal sequence is equivalent to restoring the first radar signal sequence and obtaining the corresponding second radar signal sequence. In addition, since the posterior probability model is constructed according to the physical characteristics of the radar signal itself, the posterior probability model has a high degree of matching with the physical characteristics of the radar signal, so that the part of the first radar signal sequence that matches the posterior probability model can be enhanced, but the part that does not match the posterior probability model can be suppressed, thereby making the marginal probability of the second radar signal sequence determined according to the first radar signal sequence and the preset posterior probability model more accurate. That is, the signal-to-noise ratio gain of the radar signal restored according to the posterior probability model is high, thereby improving the accuracy of the restored radar signal.

[0012] Optionally, the variables of the preset posterior probability model include variables that follow a Markov random field.

[0013] Optionally, the variables of the preset posterior probability model further include:

[0014] Variables that follow a Gaussian distribution and variables that follow a gamma distribution.

[0015] Optionally, determining the marginal probability of the second radar signal sequence according to the first radar signal sequence and the preset posterior probability model includes:

[0016] Performing iterative operations according to the first radar signal sequence, the preset posterior probability model, and the preset transfer parameter variables to obtain the marginal probability of the second radar signal sequence after iteration.

[0017] Optionally, after obtaining the second radar signal sequence, further includes:

[0018] Performing a discrete Fourier transform on the second radar signal sequence to obtain a two-dimensional graph, where the ordinate of the two-dimensional graph reflects the distance between the radar and the target, and the abscissa of the two-dimensional graph reflects the speed of the target;

[0019] Calculating the distance between the target and the radar according to the ordinate of the target in the two-dimensional graph, the speed of light, and the sweep bandwidth of the radar.

[0020] Optionally, after obtaining the second radar signal sequence, further includes:

[0021] performing a discrete Fourier transform on the second radar signal sequence to obtain a two-dimensional graph, wherein the ordinate of the two-dimensional graph reflects the distance between the radar and the target, and the abscissa of the two-dimensional graph reflects the speed of the target;

[0022] The speed of the target is calculated according to the horizontal coordinate of the target in the two-dimensional image, the sweep time of the radar, and the number of radar signals received by the radar.

[0023] In a second aspect, an embodiment of the present application provides a radar signal recovery device, comprising:

[0024] a first radar signal sequence determining module, configured to receive continuous radar signals and obtain a first radar signal sequence;

[0025] a marginal probability determination module, configured to determine a marginal probability of a second radar signal sequence based on the first radar signal sequence and a preset posterior probability model, wherein the posterior probability model is constructed based on physical characteristics of the radar signal, and the second radar signal sequence is a signal sequence obtained by recovering the first radar signal sequence;

[0026] The second radar signal sequence determination module is configured to determine a mean value of the edge probabilities to obtain the second radar signal sequence.

[0027] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the first aspects when executing the computer program.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method as described in any one of the first aspects.

[0029] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on an electronic device, enables the electronic device to execute any of the methods described in the first aspect above.

[0030] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art.

[0032] Figure 1It is a schematic flow chart of a radar signal recovery method provided by an embodiment of the present application;

[0033] Figure 2 It is a schematic structural diagram of a factor graph provided by an embodiment of the present application;

[0034] Figure 3 It is a schematic structural diagram of a factor graph based on variance inference provided by an embodiment of the present application;

[0035] Figure 4 It is provided by an embodiment of the present application and is Figure 3 The corresponding algorithm flow chart;

[0036] Figure 5 It is a schematic structural diagram of a radar signal recovery device provided by another embodiment of the present application;

[0037] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0038] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0039] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0040] It should also be understood that the term "and / or" as used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0041] In addition, in the description of the specification and appended claims of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0042] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that specific features, structures, or characteristics described in connection with that embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0043] A radar detects a target based on the propagation and reflection characteristics of electromagnetic waves. Considering that the reflected signal received by the radar (which can also be called an echo signal or a radar signal) may be affected by various factors. For example, it is affected by the distance between the radar and the target, and for another example, it is affected by the reflection ability of the target, and so on. And these effects usually increase the difficulty of recovering the reflected signal.

[0044] To improve the accuracy of the recovered reflected signal, windowing filtering processing can be performed on the reflected signal. After windowing processing, the spectrum leakage caused by the truncation of the reflected signal can be reduced, making the energy of the reflected signal more concentrated within the main lobe, thereby reducing the interference of the side lobe to other frequency components. This helps to improve the frequency selectivity of the reflected signal and indirectly enhance the processing effect of the reflected signal. However, such traditional filtering schemes are all based on the maximum likelihood estimation algorithm and cannot theoretically recover the best performance of the signal, and the signal-to-noise ratio quality obtained is still poor.

[0045] To improve the signal-to-noise ratio gain of the signal, an embodiment of this application provides a radar signal recovery method, which will be described below in conjunction with the accompanying drawings.

[0046] Figure 1 The flowchart of a radar signal recovery method provided by an embodiment of this application is shown. This type of signal recovery method can be applied to an electronic device and is described in detail as follows:

[0047] S11, Receive continuous radar signals to obtain a first radar signal sequence.

[0048] Wherein, the radar signal here is the echo signal of the radar. The radar can be a Frequency Modulated Continuous Wave (FMCW) radar, and the transmission frequency of the FMCW radar changes linearly with time.

[0049] In an embodiment of this application, the received radar signal is called the first radar signal, and the subsequently recovered radar signal is called the second radar signal to distinguish it from the unrecovered radar signal (i.e., the first radar signal).

[0050] In an embodiment of the present application, the first radar signal sequence is a sequence composed of multiple first radar signals. The multiple first radar signals may be L consecutive received radar signals, where L is greater than 1.

[0051] S12. Determine the marginal probability of the second radar signal sequence according to the first radar signal sequence and a preset posterior probability model, where the posterior probability model is constructed according to the physical characteristics of the radar signal, and the second radar signal sequence is: the signal sequence obtained by restoring the first radar signal sequence.

[0052] Among them, the physical characteristics of the radar signal include the amplitude and phase of the radar signal. Of course, other physical characteristics may also be included, which are not limited herein.

[0053] Among them, the posterior probability is a concept in Bayesian statistics, which represents the probability obtained by re-evaluating and calculating the probability of a hypothesis or event in the case of known certain data or evidence. The posterior probability is calculated according to the prior probability and the likelihood probability through Bayes' theorem. In an embodiment of the present application, the posterior probability model refers to a model for obtaining the posterior probability.

[0054] Among them, the marginal probability refers to the probability distribution that only focuses on one or several of the variables in the joint distribution of multi-dimensional random variables.

[0055] Specifically, since the posterior probability is equivalent to the prior probability, and the prior probability is approximately equal to the marginal probability, therefore, the marginal probability of the second radar signal sequence can be determined according to the first radar signal sequence and the preset posterior probability model.

[0056] S13. Determine the mean value of the marginal probability to obtain the second radar signal sequence.

[0057] Specifically, the mean value of the marginal probability of the second radar signal sequence can be obtained by integrating the second radar signal sequence and the posterior probability of the second radar signal sequence. After integrating the second radar signal sequence and the posterior probability of the second radar signal sequence, the obtained value is approximately the same as the second radar signal sequence. Therefore, determining the mean value of the marginal probability of the second radar signal sequence is equivalent to restoring the first radar signal sequence and obtaining the corresponding second radar signal sequence.

[0058] In the embodiments of the present application, since the mean value of the marginal probability of the second radar signal sequence can be obtained by integrating the second radar signal sequence and the posterior probability of the second radar signal sequence, and after integrating the second radar signal sequence and the posterior probability of the second radar signal sequence, the obtained value is approximate to the second radar signal sequence. Therefore, determining the mean value of the marginal probability of the second radar signal sequence is equivalent to restoring the first radar signal sequence and obtaining the corresponding second radar signal sequence. In addition, since the posterior probability model is constructed according to the physical characteristics of the radar signal itself, the posterior probability model has a high degree of matching with the physical characteristics of the radar signal, so that the part of the first radar signal sequence that matches the posterior probability model can be enhanced, but the part that does not match the posterior probability model can be suppressed, and further the marginal probability of the second radar signal sequence determined according to the first radar signal sequence and the preset posterior probability model is more accurate. That is, the signal-to-noise ratio gain of the radar signal restored according to this posterior probability model is high, and further the accuracy of the restored radar signal is improved.

[0059] In some embodiments, considering that in different application scenarios, the physical characteristics of the radar echo signal may vary greatly. For example, in the case of low flow velocity or zero flow velocity, there may be multiple reflections in the echo signal, and the natural river flow has a continuous velocity spectrum and a sparse distance spectrum, etc. And the Markov Random Field (MRF) has extremely strong adaptability. Therefore, in the embodiments of the present application, the variables of the preset posterior probability model can be set to include variables that follow the Markov Random Field. In this way, there is no need to independently carry out refined probability model design for each application scenario, thereby improving the radar signal restoration efficiency.

[0060] In some embodiments, the variables of the above preset posterior probability model further include:

[0061] Variables that follow the Gaussian distribution and variables that follow the gamma distribution.

[0062] Among them, the Gaussian distribution, also known as the normal distribution, is a continuous probability distribution, and its probability density function is in the shape of a bell curve and is symmetric about the mean.

[0063] Among them, the gamma distribution is a continuous probability distribution, which is often used to describe the distribution of the sum of independent random variables that have the same exponential distribution.

[0064] In the embodiments of the present application, considering that the radar echo signal interacts with various non-target objects during propagation, these non-target objects may include the ground, the ocean surface, buildings, vegetation, particles in the atmosphere, etc., and the signal obtained by interacting with non-target objects is an interference signal or a clutter signal. The existence of clutter signals will seriously affect the target detection and tracking performance of the radar. Therefore, it is necessary to suppress and process the clutter. Since the Gaussian distribution and the gamma distribution can be used to generate clutter signals that conform to specific statistical characteristics. For example, a correlated random sequence is generated through the Gaussian distribution, and then a clutter signal that conforms to the gamma distribution is obtained through a non-linear transformation. Therefore, the variables of the posterior probability model also include variables that follow the Gaussian distribution and variables that follow the gamma distribution, which is beneficial to enhancing the robustness of the posterior probability model and enabling it to be applicable to a wider range of application scenarios.

[0065] To more clearly describe the posterior probability model provided by the embodiments of the present application, the following will be described with a specific example.

[0066] Suppose the radar system uses the parameters of N-point DFT (receiving N complex data points per frame) and L consecutive frames of reception (receiving L consecutive frames at a time). The true two-dimensional Doppler spectrum corresponding to the distance and speed of the radar illuminating the target is X ∈ C N×L (dimension 1 reflects speed and dimension 2 reflects distance). Then the radar echo time-domain signal collected by the analog-to-digital converter (ADC) of the radar system is:

[0067]

[0068] where Y ∈ C N×L is the radar received signal, that is, the echo signal received by the radar (or the radar signal), represents the N-point inverse Fourier transform matrix, represents the L-point inverse Fourier transform matrix, Z ∈ C N×L represents the Gaussian noise of the radar system, where I is the identity matrix. For the convenience of subsequent derivation, we use the Kronecker product (if the Kronecker product is not used, the form of the linear equation expression is AXB, where X is the unknown, and the observation matrices are on both sides of the unknown, and single matrix operations cannot be performed. By using the Kronecker product, both the A and B observation matrices can act on the left side of the unknown matrix X, facilitating single matrix operations), so that where A ∈ C NL×NL . Therefore, we rewrite formula (1) as:

[0069] vec(Y) = A · vec(X) + vec(Z)......................(2).

[0070] For the convenience of subsequent formula derivation, let y v = vec(Y), x v = vec(X), z v = vec(Z). For general radio frequency systems (such as communication systems, radar systems), the index for evaluating the signal recovery ability is the mean squared error (MSE). Assume that the recovered radar received signal (i.e., the second radar signal) is We hope that the designed radar signal recovery algorithm or radar signal filter satisfies the following conditions:

[0071]

[0072] Among them, "arg(*)" is an operation symbol used to represent the operation of solving its function, and "min(*)" is the minimum function operation. According to formula derivation, it can be known that The optimal solution of can be written as the following formula:

[0073]

[0074] Among them, is called the posterior mean, and p(x v |y v ) is the posterior probability density function. Since the function of formula (3) is the quadratic loss function, when dealing with this function, the minimum mean square error (MMSE) is equivalent to MAP. Therefore, the optimization problem in formula (3) can be equivalently converted into a maximum a posteriori (MAP) problem, that is:

[0075]

[0076] According to Bayes' theorem, the posterior probability can be expressed as:

[0077] p(x v |y v ) ∝ p(y v |x v )p(x v )......................(6).

[0078] Among them, "∝" means proportional to. The above formula (6) means that "p(x v |y v )" is proportional to "p(y v |x v )p(xv )”. “p(y v |x v )” is the likelihood probability, which is a Gaussian distribution with a mean equal to Ax v and a variance equal to σ 2 I NL .” “p(x v )” is the prior probability, and “p(y v |x v )p(x v )” can directly describe the physical characteristics of the true two-dimensional Doppler spectrum x corresponding to the distance and speed of the radar illuminating the target v . This physical characteristic represents the correlation and continuity of the distance and speed after being converted into the two-dimensional Doppler spectrum. Since the physical characteristics of the radar echo may vary greatly in different application scenarios, an extremely adaptable Markov random field (MRF) is proposed as the prior probability model. At the same time, to enhance the robustness of the model, a Gaussian distribution and a Gamma distribution are added on the basis of the MRF to make it applicable to a wider range of application scenarios. For the convenience of explanation, we use a factor graph (see Figure 2 ) to describe the prior probability model.

[0079] In Figure 2 , each circle represents a random variable, and the relevant random variables are connected by straight lines. For the set of random variables {s l,n |l = 1,…,L, n = 1,…N}, any random variable s l,n is connected to ρ l,n l,n , and any s l,n has no more than four adjacent nodes in the same set. The formula for the prior probability model corresponding to the factor graph is:

[0080]

[0081] where s v = vec(S), S ∈ {-1, 1} L×N is a random variable obeying the MRF distribution, ρ v = vec(P), P ∈ C L×N is a random variable obeying the Gamma distribution, X v = [x v,1 ,…,x v,NL T ,

[0082] s v = [s v,1 ,…,s v,NL T , ρ v = [ρ v,1 ,…,ρ​v,NL T The expression of the first term of the prior probability model is as follows:

[0083]

[0084] The expression of the second term of the prior probability model is as follows:

[0085]

[0086] where a m , b m , are the distribution parameters of the Gamma distribution.

[0087] The expression of the third term of the prior probability is as follows:

[0088]

[0089] where represents the adjacent node of any point s l,n in the two-dimensional second-order Markov random field, and both β and α are known. Then, the posterior probability model for the FMCW radar echo can be as follows:

[0090]

[0091]

[0092] This formula (11) is a posterior probability model provided by an embodiment of the present application.

[0093] To obtain the estimated value the expectation-maximization (EM) algorithm can be used to solve the maximum a posteriori problem of formula (5). Optionally, considering that the posterior probability model (11) corresponding to formula (5) is relatively complex and an analytical solution cannot be written, if the EM algorithm is directly used for solving, there are usually obstacles in engineering implementation. Also considering that the posterior probability is equivalent to the prior probability, and the prior probability is approximated to the marginal probability, therefore, the solution of the posterior probability model can be transformed into the solution of the marginal probability.

[0094] In some embodiments, determining the marginal probability of the second radar signal sequence according to the first radar signal sequence and the preset posterior probability model includes:

[0095] Performing iterative operations according to the first radar signal sequence, the preset posterior probability model, and the preset transfer parameter variable to obtain the marginal probability of the second radar signal sequence after iteration.

[0096] ​Optionally, the range of the preset transfer parameter variable may be [-1, 1]. When the variables in the posterior probability model include variables subject to a Markov random field, the transfer parameter variable can be determined according to the variables given by the joint probability of the Markov field (Equation 10).

[0097] In the embodiments of the present application, since the marginal probability of the second radar signal sequence is determined by an iterative method, and the iterative method can gradually approximate the optimal solution based on the current state and local information and can more conveniently adapt to dynamic changes, therefore, the iterative method is beneficial to improving the accuracy and simplicity of the determined marginal probability.

[0098] According to Equation (4), Since the subsequent derivation of p(x v |y v ) is too complex, therefore, the mean value cannot be solved by integration. Therefore, we need to construct The construction principle is to minimize the KL-Divergence, that is, q(x v ) = argmin KL(q(x v ) || p(x v ))), and then μ is obtained according to message passing. Due to Bayes' theorem: p(x v ) ∝ p(x v |y v ), and q(x v ) ≈ p(x v ), therefore The following describes how to determine the marginal probability of the second radar signal sequence in combination with a specific example.

[0099] Refer to Figure 3 , Figure 3 The algorithm framework corresponding to the radar signal recovery method provided in the embodiments of the present application is described in the form of a factor graph. In Figure 3 , each square corresponds to a probability function on the right side of Equation (11), and the algorithm flow chart corresponding to Figure 3 is as shown in Figure 4 .

[0100] In Figure 4 , the factor graph message passing algorithm includes three loop iterations: inner iteration 1, inner iteration 2, and outer iteration 1. Among them, outer iteration 1 is the iteration corresponding to the change of the preset transfer parameter variable. In these three loop iterations, q(x v ), q(ρ v ), and q(s v ) are collectively referred to as marginal probabilities, but the marginal probability of the second radar sequence is Figure 4 The q(x output after the end of outer iteration 1 and the end of inner iteration 2 inv ) Considering that the more iterations there are, the greater the algorithm overhead, therefore, the inner loop 1 and the inner loop 2 can perform a fixed number of iterations (such as 5 times) to control the algorithm overhead, while the outer iteration 1 can choose to perform a fixed number of iterations or iterate according to a fixed algorithm convergence criterion. The formula for message passing is as follows:

[0101]

[0102] Among them, μ and Σ are the mean and covariance matrix of the Gaussian distribution respectively. Their formulas are as follows:

[0103]

[0104]

[0105] Among them, and are the updated Gamma distribution parameters respectively, and the parameter update of the Gamma distribution is calculated using the EM algorithm:

[0106]

[0107] Among them, μ m and Σ m are the results obtained from the previous iteration loop calculation.

[0108] The formula for the q(ρ v ) message is expressed as follows:

[0109]

[0110] The formula for the q(s v ) message is expressed as follows:

[0111]

[0112] Among them, π m is the input message of the s v,m node, and its formula is:

[0113]

[0114] Among them, is the output information of the node s v,m = 1. Here, the Checkmate-box algorithm and the Gibbs-sampling method are adopted, and its formula is:

[0115]

[0116] Among them, λ v,m,sFor the internal message passing of the Markov field, it can be given by the joint probability of the Markov field (Formula 10) through Bayesian inference, which is equivalent to a definite value.

[0117] The following combines Formula (13) to Formula (18) and Figure 4 describes the process of calculating the marginal probability of the second radar sequence.

[0118] Specifically, first and are given initial values. σ is the variance corresponding to the noise, which is a known value, and A is a known regular matrix. Then, according to Formula (13), Σ can be calculated. Based on this Σ and Formula (14), μ is calculated. After that, q(ρ v ), q(s v ) and π m are gradually derived according to Formula (15), Formula (16) and Formula (17). After obtaining π m , Σ and μ are derived in the order of Formula (16), Formula (15), Formula (13), and Formula (14). The above steps are repeated, and the loop corresponding to these steps is Figure 4 the internal iteration 1.

[0119] After the internal iteration 1 ends, the input α v,m,s (this λ v,m,s is the message automatically iteratively updated inside the Markov field) is adjusted according to Formula (18), and the internal iteration 2 is executed. After the internal iteration 2 ends, the output value will enter the external iteration 1. When the internal iteration 1, the internal iteration 2, and the external iteration 1 all end iteratively, the marginal probability of the second radar sequence will be output. Based on the marginal probability of the second radar sequence, μ is determined, and this μ is the restored radar signal

[0120] In some embodiments, after obtaining the above second radar signal sequence, it further includes:

[0121] A1. Perform a discrete Fourier transform on the above second radar signal sequence to obtain a two-dimensional graph. The ordinate of the above two-dimensional graph reflects the distance between the radar and the target, and the abscissa of the above two-dimensional graph reflects the speed of the above target.

[0122] A2. Calculate the distance between the above target and the above radar according to the ordinate of the target in the above two-dimensional graph, the speed of light, and the sweep bandwidth of the above radar.

[0123] A3. Calculate the speed of the above target according to the abscissa of the target in the above two-dimensional graph, the sweep duration of the above radar, and the number of radar signals received by the above radar.

[0124] In the embodiments of the present application, according to the restored the converted target distance and speed information. Among them, the the corresponding two-dimensional diagram is L rows and N columns. Where the ordinate (corresponding to the column) is converted to distance, and the abscissa (corresponding to the row) is converted to speed. Assume x l,n is a valid target (i.e., >> noise power), then, the distance and speed can be converted from the coordinate position of this point.

[0125] Assume that the sweep bandwidth of the radar system is B (Hz), the sweep duration is T (s), and N C radar signals are continuously transmitted and received. Then the distance is converted to: d = n□C / (2B), where C is the speed of light. The speed is converted to: v = l□λ / (2TN C ).

[0126] In the embodiments of the present application, since the restored according to the above method is more accurate, therefore, the more accurate target distance and speed can be determined according to the

[0127] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0128] Corresponding to the radar signal restoration method described in the above embodiments, Figure 5 the structural block diagram of the radar signal restoration device provided by the embodiments of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0129] Referring to Figure 5 , the radar signal restoration device 5 includes:

[0130] The first radar signal sequence determination module 51 is configured to receive continuous radar signals and obtain a first radar signal sequence.

[0131] The edge probability determination module 52 is configured to determine the edge probability of the second radar signal sequence according to the above first radar signal sequence and a preset posterior probability model, where the above posterior probability model is constructed according to the physical characteristics of the radar signal, and the above second radar signal sequence is: the signal sequence obtained by restoring the above first radar signal sequence.

[0132] The second radar signal sequence determination module 53 is configured to determine the mean value of the above edge probability to obtain the above second radar signal sequence.

[0133] In the embodiments of the present application, since the mean value of the marginal probability of the second radar signal sequence can be obtained by integrating the second radar signal sequence and the posterior probability of the second radar signal sequence, and the value obtained after integrating the second radar signal sequence and the posterior probability of the second radar signal sequence is approximate to the second radar signal sequence, therefore, determining the mean value of the marginal probability of the second radar signal sequence is equivalent to restoring the first radar signal sequence and obtaining the corresponding second radar signal sequence. In addition, since the posterior probability model is constructed according to the physical characteristics of the radar signal itself, the posterior probability model has a high degree of matching with the physical characteristics of the radar signal, so that the part of the first radar signal sequence that matches the posterior probability model can be enhanced, but the part that does not match the posterior probability model can be suppressed, thereby making the marginal probability of the second radar signal sequence determined according to the first radar signal sequence and the preset posterior probability model more accurate. That is, the signal-to-noise ratio gain of the radar signal restored according to the posterior probability model is high, thereby improving the accuracy of the restored radar signal.

[0134] Optionally, the variables of the above preset posterior probability model include variables that follow a Markov random field.

[0135] Optionally, the variables of the above preset posterior probability model further include: variables that follow a Gaussian distribution and variables that follow a gamma distribution.

[0136] Optionally, the above marginal probability determination module is specifically configured to:

[0137] Perform iterative operations according to the above first radar signal sequence, preset posterior probability model, and preset transfer parameter variables to obtain the marginal probability of the above second radar signal sequence after iteration.

[0138] Optionally, the radar signal restoration device 5 further includes:

[0139] A discrete Fourier transform module, configured to perform a discrete Fourier transform on the above second radar signal sequence after obtaining the second radar signal sequence, to obtain a two-dimensional map, where the ordinate of the two-dimensional map reflects the distance between the radar and the target, and the abscissa of the two-dimensional map reflects the speed of the target.

[0140] A distance calculation module, configured to calculate the distance between the target and the radar according to the ordinate of the target in the two-dimensional map, the speed of light, and the sweep bandwidth of the radar.

[0141] Optionally, the radar signal restoration device 5 further includes:

[0142] A discrete Fourier transform module is used to perform a discrete Fourier transform on the second radar signal sequence obtained above to obtain a two-dimensional graph. The ordinate of the two-dimensional graph reflects the distance between the radar and the target, and the abscissa of the two-dimensional graph reflects the speed of the target.

[0143] A speed calculation module is used to calculate the speed of the target according to the abscissa of the target in the two-dimensional graph, the sweep duration of the radar, and the number of radar signals received by the radar.

[0144] It should be noted that for the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiments of the present application, their specific functions and the technical effects brought can be specifically referred to in the method embodiment part, and will not be elaborated here.

[0145] Figure 6 This is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 6 shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 6 only one processor is shown in the figure), a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the steps in any of the above method embodiments are implemented.

[0146] The electronic device 6 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The electronic device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 6 this is only an example of the electronic device 6 and does not constitute a limitation on the electronic device 6. It may include more or fewer components than those shown in the figure, or combine some components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0147] The so-called processor 60 may be a central processing unit (CPU). The processor 60 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0148] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 6. Further, the memory 61 may also include both the internal storage unit and the external storage device of the electronic device 6. The memory 61 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or will be output.

[0149] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated here.

[0150] An embodiment of the present application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, the steps in any of the foregoing method embodiments are implemented.

[0151] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in any of the foregoing method embodiments can be implemented.

[0152] An embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in any of the foregoing method embodiments when executed.

[0153] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to the photographing device / electronic device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0154] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0155] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0156] In the embodiments provided by the present application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in an electrical, mechanical, or other form.

[0157] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A radar signal recovery method, characterized in that including: Receiving continuous radar signals to obtain a first radar signal sequence; Determining the marginal probability of a second radar signal sequence according to the first radar signal sequence and a preset posterior probability model, wherein the posterior probability model is constructed according to the physical characteristics of radar signals, and the second radar signal sequence is: a signal sequence obtained by restoring the first radar signal sequence; Determining the mean value of the marginal probability to obtain the second radar signal sequence.

2. The radar signal recovery method according to claim 1, wherein The variables of the preset posterior probability model include variables subject to a Markov random field.

3. The radar signal recovery method according to claim 2, wherein The variables of the preset posterior probability model further include: Variables subject to a Gaussian distribution and variables subject to a gamma distribution.

4. The radar signal recovery method according to claim 2, characterized in that, The determining the marginal probability of the second radar signal sequence according to the first radar signal sequence and the preset posterior probability model includes: Performing iterative operations according to the first radar signal sequence, the preset posterior probability model, and preset transfer parameter variables to obtain the marginal probability of the second radar signal sequence after iteration.

5. The radar signal recovery method according to any one of claims 1 to 4, characterized in that After obtaining the second radar signal sequence, it further includes: Performing a discrete Fourier transform on the second radar signal sequence to obtain a two-dimensional map, where the ordinate of the two-dimensional map reflects the distance between the radar and the target, and the abscissa of the two-dimensional map reflects the speed of the target; Calculating the distance between the target and the radar according to the ordinate of the target in the two-dimensional map, the speed of light, and the sweep bandwidth of the radar.

6. The radar signal recovery method according to any one of claims 1 to 4, characterized in that After obtaining the second radar signal sequence, it further includes: Performing a discrete Fourier transform on the second radar signal sequence to obtain a two-dimensional map, where the ordinate of the two-dimensional map reflects the distance between the radar and the target, and the abscissa of the two-dimensional map reflects the speed of the target; Calculating the speed of the target according to the abscissa of the target in the two-dimensional map, the sweep duration of the radar, and the number of radar signals received by the radar.

7. A radar signal recovery device, characterized in that, including: A first radar signal sequence determination module, configured to receive continuous radar signals to obtain a first radar signal sequence; A marginal probability determination module, configured to determine the marginal probability of a second radar signal sequence according to the first radar signal sequence and a preset posterior probability model, wherein the posterior probability model is constructed according to the physical characteristics of radar signals, and the second radar signal sequence is: a signal sequence obtained by restoring the first radar signal sequence; A second radar signal sequence determination module, configured to determine the mean value of the marginal probability to obtain the second radar signal sequence.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that, Including a computer program, when the computer program is run, the method according to any one of claims 1 to 6 is executed.

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