Distributed radar target positioning method and device, communication equipment and storage medium

Abstract

The invention relates to a distributed radar target positioning method and device, communication equipment and a storage medium, which are applied to a distributed multiple-input multiple-output radar positioning system. The method comprises the following steps: under the condition of detecting a target, constructing a residual matrix according to a bistatic distance measurement matrix, a first distance matrix between a transmitter and the target and a second distance matrix between a receiver and the target; establishing a non-convex norm minimization problem model according to the residual matrix; and approximately solving the non-convex norm minimization problem model by using a first generalized iterative reweighting algorithm to obtain a target positioning result. According to the method, the non-convex norm with the residual matrix is modeled into the target loss function, and the corresponding generalized iterative reweighting algorithm is developed to solve the non-convex minimization problem, so that the positioning precision of the MIMO radar in low SINR and NLOS abnormal environments can be ensured.

Description

Technical Field

[0001] The present application relates to the field of target positioning technology, and in particular to a distributed radar target positioning method, apparatus, communication equipment, and storage medium. Background Art

[0002] When distributed Multiple-Input Multiple-Output (MIMO) radar positioning systems locate targets, electromagnetic wave signals emitted by the radar may be reflected, refracted, or transmitted due to obstruction by urban buildings, vehicles, pedestrians, and trees. This can cause a large number of outliers in the measurement data obtained by the radar receiver. These outliers are commonly known as non-line-of-sight (NLOS) anomalies. Furthermore, in urban and adversarial environments, radar systems are often subject to various intentional or unintentional electromagnetic interference, such as interference from radio or television signals. This can lead to anomalies in low signal-to-interference-plus-noise ratio (SINR) measurements.

[0003] Both low SINR abnormal measurement and NLOS abnormal measurement will seriously affect the performance of traditional target positioning methods, resulting in a significant decrease in positioning accuracy and even complete failure of the method.

[0004] Therefore, there is an urgent need for a positioning solution that can effectively improve the positioning accuracy of the MIMO radar positioning system. Summary of the Invention

[0005] Based on this, it is necessary to provide a distributed radar target positioning method, device, communication equipment and storage medium that can effectively complete target positioning and improve target positioning accuracy in the presence of low SINR anomalies and NLOS anomaly measurements in complex urban physical and electromagnetic environments to address the above technical problems.

[0006] In a first aspect, the present application provides a distributed radar target positioning method, which is applied to a distributed multi-input multi-output radar positioning system; the method comprises: In the case of detecting a target, a residual matrix is ​​constructed based on a bistatic distance measurement matrix, a first distance matrix between a transmitter and a target, and a second distance matrix between a receiver and a target; According to the residual matrix, a non-convex Norm minimization problem model; The first generalized iterative reweighted algorithm is used to approximately solve the non-convex The norm minimization problem model is used to obtain the target positioning result.

[0007] In one embodiment, constructing a residual matrix based on a bistatic distance measurement matrix, a first distance matrix between a transmitter and a target, and a second distance matrix between a receiver and a target includes: in, , , , , is the residual matrix, is the bibasic distance measurement matrix, For the Bistatic range measurements for a transmitter-receiver pair, is the first distance matrix, is the second distance matrix.

[0008] In one embodiment, the non-convex Norm minimization problem models, including: in, Represents the residual matrix norm, is the target location, is the Euclidean distance between the transmitter and the target, is the Euclidean distance between the receiver and the target.

[0009] In one embodiment, the non-convex Norm minimization problem model, also includes: The non-convex Norm The value is set to .

[0010] In one embodiment, the non-convex The norm minimization problem model is used to obtain the target positioning results, including: Initializing algorithm parameters in the first generalized iterative reweighted algorithm, wherein the algorithm parameters include the number of iterations, the position estimate, and the weight; Iteratively solving the weighted minimization problem until the algorithm converges or the number of iterations is greater than or equal to a preset iteration threshold, and using the position estimate value obtained by the last iterative solution of the weighted minimization problem as the target positioning result; wherein, during each iteration, the weight is updated according to the first weight update algorithm; The first weight updating algorithm includes: in, , , For the The weights when solving the weighted minimization problem in the iteration, For the The residual error when iteratively solving the weighted minimization problem, is the regularization parameter, For the Bistatic range measurements for a transmitter-receiver pair, For the The first step in iteratively solving the weighted minimization problem The Euclidean distance between the transmitter and the target, For the The first step in iteratively solving the weighted minimization problem The Euclidean distance between the receiver and the target, For the non-convex Norm value.

[0011] In one embodiment, the non-convex After the norm minimization problem model is established, the method further comprises: The second generalized iterative reweighted algorithm is used to approximately solve the non-convex -norm minimization problem model to obtain the target positioning result.

[0012] In one embodiment, the second generalized iterative reweighted algorithm is used to approximately solve the non-convex -norm minimization problem model to obtain target positioning results, including: Initializing algorithm parameters in the second generalized iterative reweighted algorithm, wherein the algorithm parameters include the number of iterations, the position estimate, and the weight; Iteratively solving the weighted minimization problem until the algorithm converges or the number of iterations is greater than or equal to a preset iteration threshold, and using the position estimate value obtained by the last iterative solution of the weighted minimization problem as the target positioning result; wherein, during each iteration, the weight is updated according to the second weight update algorithm; The second weight updating algorithm includes: in, , , The first weight update algorithm The weights when solving the weighted minimization problem in the iteration, The second weight update algorithm The weights when solving the weighted minimization problem in the iteration, For the The residual error when iteratively solving the weighted minimization problem, For the The weight matrix when solving the weighted minimization problem for iterations.

[0013] In a second aspect, the present application further provides a distributed radar target positioning device, which is applied to a distributed multi-input multi-output radar positioning system; the device comprises: a matrix construction module for constructing a residual matrix according to a bistatic distance measurement matrix, a first distance matrix between a transmitter and a target, and a second distance matrix between a receiver and a target when detecting a target; A model building module is used to build a non-convex Norm minimization problem model; The target positioning module is used to approximately solve the non-convex The norm minimization problem model is used to obtain the target positioning result.

[0014] In a third aspect, the present application also provides a communication device, including a distributed multi-input multi-output radar positioning system, a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the distributed radar target positioning method described in the first aspect.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the distributed radar target positioning method described in the first aspect.

[0016] In a fifth aspect, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the distributed radar target positioning method described in the first aspect.

[0017] In summary, the present application proposes a distributed radar target positioning method, apparatus, communication device and storage medium, which are applied to a distributed multi-input multi-output radar positioning system; the method comprises: when detecting a target, constructing a residual matrix based on a bistatic distance measurement matrix, a first distance matrix between a transmitter and a target, and a second distance matrix between a receiver and a target; establishing a non-convex norm minimization problem model; using the first generalized iterative reweighted algorithm to approximately solve the non-convex The norm minimization problem model is used to obtain the target positioning result. This application uses the non-convex residual matrix The norm is modeled as the target loss function, and the corresponding generalized iterative reweighted algorithm is developed to solve the non-convex minimization problem, which can ensure the positioning accuracy of MIMO radar in low SINR and NLOS abnormal environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 1 is a flow chart of a distributed radar target positioning method according to an embodiment; Figure 2 1 is a flow chart of a distributed radar target positioning method according to another embodiment; Figure 3 For an embodiment, different Penalty function under value With scalar Schematic diagram of the relationship between Figure 4 This is one of the simulation experiment result diagrams in one embodiment; Figure 5 FIG2 is a second diagram of simulation experiment results in one embodiment; Figure 6 Figure 3 is a simulation experiment result in one embodiment; Figure 7 FIG4 is a simulation experiment result diagram in one embodiment; Figure 8 is a structural block diagram of a distributed radar target positioning device in one embodiment; Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0020] In related technologies, both low signal-to-interference-plus-noise ratio (SINR) and non-line-of-sight (NLOS) measurements can severely impact the performance of traditional target localization methods, leading to a significant decrease in positioning accuracy or even complete failure. Several solutions have been introduced to mitigate outliers in measurement data in the fields of source localization and wireless sensor network positioning. The most common approach is to identify and remove outlier observations. However, conventional outlier identification methods are ineffective, resulting in unreliable results. Recently, machine learning-based methods have been used to achieve reliable outlier identification. However, these methods rely on tedious prior experimental database construction, which consumes significant human resources and time. Furthermore, these methods suffer from poor generalization, limiting their practical application. Furthermore, most work focuses solely on NLOS anomalies, which introduce positive error terms, while lacking research on low SINR anomalies, which can introduce both positive and negative error terms. However, low SINR anomalies are highly prevalent in radar target localization in urban environments.

[0021] To solve the above problems, this embodiment provides a distributed radar target positioning method, which converts the non-convex The norm is modeled as the target loss function. Based on this robust model, two generalized iterative reweighting (IRW) algorithms are developed to solve non-convex minimization problems, significantly improving the accuracy of MINO radar target positioning in low SINR and NLOS anomaly environments.

[0022] In one embodiment, reference Figure 1 , provides a distributed radar target positioning method, which is described using the method applied to a distributed multiple-input multiple-output (MIMO) radar positioning system as an example. The method includes the following steps: S101 , when detecting a target, constructing a residual matrix according to a bistatic distance measurement matrix, a first distance matrix between a transmitter and the target, and a second distance matrix between a receiver and the target.

[0023] In this embodiment, the case of detecting a target refers to an application scenario in which a MIMO radar positioning system is used to locate a target to be detected. In a specific application process, the target can specifically refer to a target to be detected in a low SINR and NLOS abnormal environment, or a target to be detected in a normal noise environment. Preferably, the distributed radar target positioning method provided in this embodiment is enabled when it is determined that the target to be detected is in a low SINR and NLOS abnormal environment, which can maximize the positioning accuracy of the target to be detected while increasing the algorithm configurability and flexibility of the MIMO radar positioning system.

[0024] In one embodiment, constructing a residual matrix based on a bistatic distance measurement matrix, a first distance matrix between a transmitter and a target, and a second distance matrix between a receiver and the target includes: in, , , , , is the residual matrix, is the bi-basic distance measurement matrix, For the Bistatic range measurements for a transmitter-receiver pair, is the first distance matrix, is the second distance matrix.

[0025] S102, establish a non-convex matrix based on the residual matrix Norm minimization problem model.

[0026] In this embodiment, the non-convex The expression of the norm minimization problem model is: in, Residual matrix norm, is the target location, is the Euclidean distance between the transmitter and the target, is the Euclidean distance between the receiver and the target.

[0027] S103, using the first generalized iterative reweighted algorithm to approximately solve the non-convex The norm minimization problem model is used to obtain the target positioning result.

[0028] In this embodiment, the first generalized iterative reweighting algorithm, namely -IRW algorithm.

[0029] In this embodiment, the specific steps of the first generalized iterative reweighting algorithm include: Initializing algorithm parameters in the first generalized iterative reweighted algorithm, wherein the algorithm parameters include the number of iterations, the position estimate, and the weight; Iteratively solving the weighted minimization problem until the algorithm converges or the number of iterations is greater than or equal to a preset iteration threshold, and using the position estimate obtained by the last iterative solution of the weighted minimization problem as the target positioning result; wherein, in each iteration process, the weight is updated according to the first weight update algorithm; The first weight update algorithm includes: in, , , For the The weights when solving the weighted minimization problem in the iteration, For the The residual error when iteratively solving the weighted minimization problem, is the regularization parameter, For the Bistatic range measurements for a transmitter-receiver pair, For the The first step in iteratively solving the weighted minimization problem The Euclidean distance between the transmitter and the target, For the The first step in iteratively solving the weighted minimization problem The Euclidean distance between the receiver and the target, is non-convex Norm value.

[0030] In one embodiment, Figure 2 As shown, S102 establishes a non-convex After the norm minimization problem model, the distributed radar target positioning method also includes: S104, using the second generalized iterative reweighted algorithm to approximate the solution of non-convex -norm minimization problem model to obtain the target positioning result.

[0031] In this embodiment, the second generalized iterative reweighting algorithm, namely -IIRW algorithm.

[0032] In this embodiment, the specific steps of the second generalized iterative reweighting algorithm include: Initializing algorithm parameters in the second generalized iterative reweighted algorithm, wherein the algorithm parameters include the number of iterations, the position estimate, and the weight; Iteratively solving the weighted minimization problem until the algorithm converges or the number of iterations is greater than or equal to a preset iteration threshold, and using the position estimate obtained by the last iterative solution of the weighted minimization problem as the target positioning result; wherein, in each iteration process, the weight is updated according to the second weight update algorithm; The second weight update algorithm includes: in, , , The first weight update algorithm The weights when solving the weighted minimization problem in the iteration, The second weight update algorithm The weights when solving the weighted minimization problem in the iteration, For the The residual error when iteratively solving the weighted minimization problem, For the The weight matrix when solving the weighted minimization problem for iterations.

[0033] The following describes in detail the implementation of the steps of the distributed radar target positioning method provided in this embodiment by taking a specific scenario as an example: Consider dimensional space using a distributed MIMO radar system for target positioning, where In this embodiment, the MIMO radar system includes transmitters and receivers, of which The location of each transmitter is used express, The location of each receiver is used It should be noted that, assuming that a single target in the region of interest is located, its position is represented by express.

[0034] In actual application, by utilizing the orthogonal separability of the transmitted waveform, each receiver can measure the radiation emitted from different transmitters and then scattered by the target. Therefore, we can use Receivers total TD measurement and signal propagation speed Multiply to get Bistatic Range (BR) measurement information. The transmitter and The actual BR measurement information of a receiver pair can be calculated according to the following formula: in, , , For the The transmitter and The real BR measurement information of the receiver pair, For the The Euclidean distance between the transmitter and the target, For the The Euclidean distance between the receiver and the target. Specifically, and The calculation formula is as follows: in, is the target position, For the The location of the transmitter, For the The location of the receiver.

[0035] In practical application environments, signals often include non-line-of-sight propagation, electromagnetic interference, and measurement noise. The observed BR measurement can be modeled as the following equation: in, represents zero-mean Gaussian noise, Indicates possible low SINR or NLOS anomaly errors.

[0036] The task of target localization is to measure the Estimate the position of unknown targets as accurately as possible The task of object localization is a challenging problem, not only because Relative to is nonlinear and nonconvex, especially due to the presence of outlier measurements.

[0037] In related technologies, the traditional Gaussian least squares method usually assumes that there are no outliers, that is, = In this case, the maximum likelihood estimation of the target position is equivalent to the least squares estimation. According to the least squares (LS) criterion, the positioning problem can be expressed as follows: Combine and The calculation formula of , the unconstrained optimization problem in the above formula can be transformed into the following constrained optimization problem: in, , , , .

[0038] However, in the presence of abnormal measurements, the performance of the above least square norm based method will be significantly reduced. In order to alleviate the impact of outliers, the prior art proposes a method based on The robust model of the norm is as follows: Although based on Compared with the method based on minimum The norm method is more robust. However, when there are multiple abnormal observations, The performance of the norm method is still very unsatisfactory.

[0039] In this embodiment, the target positioning problem is described as a non-convex norm minimization problem, and then proposed two generalized iterative reweighting algorithms to deal with this non-convex Optimization problem of norm.

[0040] According to the above optimization model, the target positioning problem is reformulated as follows Norm minimization problem: In order to make the above expression more compact, a new matrix is ​​introduced: in, and All dimensional matrix, and define the "residual matrix" as follows: in, is a dimensional matrix, its The elements are .also, Also a dimensional matrix, its The elements are , called the "BR measurement matrix". The optimization problem can be expressed as: in, Representation matrix of Norm. It should be noted that when hour, The optimization problem can be simplified as or In this embodiment, the non-convex Norm The value is set to Because in In this case, the target positioning method provided by this embodiment can provide better robust performance.

[0041] The following aims to intuitively explain why the proposed non-convex Norm models are better than convex ones (e.g. and Norm) model has better anti-outlier properties. It should be emphasized that Compared with the norm, The norm cost function amplifies the impact of outliers because it uses squared error as the optimization criterion. Intuitively, Compared with the norm, The norm cost function converts the error The square process ( contrast ), the contribution of outliers to the cost function is greater than that of normal observations. In order to minimize the cost function, In the case of the norm, more attention will be paid to the measurement of outliers, resulting in the neglect of normal observations. Therefore, The norm cost function is more susceptible to abnormal measurements.

[0042] like Figure 3 (The horizontal axis is the residual , the vertical axis is the penalty function (penalty)) shown, Class penalty function in has infinite slope at , and its convex relaxation ( and The function has unit and square slope at the origin. However, for Concave penalty function The slope at the origin is When growing rapidly. This allows data with smaller residuals (good data) to receive relatively greater attention, while data with larger residuals (bad data) receive relatively less attention. The optimization of the norm is better than that based on convex Norm and The optimization of the norm can better resist the influence of outliers, which is consistent with the conclusion that low-order moments are more resistant to outliers.

[0043] In addition, the robustness of the proposed model to outliers can also be understood from the perspective of sparsity. Assume that only a small portion of BR measurements are contaminated by outliers, that is, the outlier matrix is sparse, when the noise When is small and can be neglected, it is reasonable to assume that the residual matrix is ​​sparse. Norm and Compared with the norm, non-convex The norm is considered closer to norm and is widely used in sparse recovery and robust perception.

[0044] However, due to the non-convex norm objective function and non-convex quadratic constraints, The optimization problem in the formula is difficult to solve directly. This embodiment provides a generalized iterative reweighting ( -IRW) algorithm to approximate the solution Where the residual matrix with non-convex quadratic constraints is norm minimization problem.

[0045] In this embodiment, -The specific implementation steps of the IRW algorithm can be summarized as follows: Step 1: Initialize the number of iterations, target position and weight parameters, including but not limited to initialization , , , .

[0046] Step 2, solve the weighted minimization problem: in, , , , .

[0047] Step 3, update the weights: in, .

[0048] Step 4: When the algorithm converges or Reaching the preset maximum number of iterations Otherwise, , then skip to step 2.

[0049] because The existence of non-differentiability of norm and non-convex quadratic constraints, solving The subproblem in the formula is not easy.

[0050] Therefore, in order to adapt to the solution of this sub-problem, a new normalized weight update method is proposed, which results in an improved iterative reweighting ( -IIRW) algorithm, as follows: In this embodiment, The specific implementation steps of the IIRW algorithm can be summarized as follows: Step 1: Initialize the number of iterations, target position and weight parameters, including but not limited to initialization , , , .

[0051] Step 2, solve the weighted minimum problem: in, , , , .

[0052] Step 3, update the weights: in, ;and , where the weight matrix .

[0053] Step 4: When the algorithm converges or Reaching the preset maximum number of iterations Otherwise, , then jump to step 2. It should be noted that the preset maximum number of iterations That is, the preset iteration number threshold in the aforementioned embodiment.

[0054] It should be noted that -Parameters in step 3 of the IRW algorithm >0 is called the regularization parameter, which can be used to avoid infinite weights when the denominator is zero. However, in order to ensure the accuracy of the algorithm, the parameter The value of is generally very small. At this time, it can be found that the value range of the weight is still very large.

[0055] exist -In the IIRW algorithm, the weights are constrained to between, making it more suitable for -LPNN solver. Numerical experiments show that the parameters should be set slightly smaller than the expected magnitude of the non-zero residual. .

[0056] -IIRW algorithm can not only optimize the solution of subproblems, -IIRW algorithm also does not need to tune the regularization parameters. Regarding the convergence of the proposed algorithm, -IRW algorithm and -IIRW algorithms are based on iterative reweighting algorithms and -LPNN solver, while -LPNN solvers have been shown to converge to local minima, so the proposed algorithm will follow the convergence properties of the IRW algorithm.

[0057] In summary, this embodiment provides a distributed radar target positioning method. To solve the problem that the low SINR anomaly and NLOS anomaly measurement in the complex physical and electromagnetic environment of the city cause the distributed radar target positioning accuracy to drop significantly or even fail completely, a non-convex - Minimization and iterative reweighting enhanced robust target localization method. This method defines the residual matrix and models the traditional target localization problem as a non-convex residual matrix with a non-convex quadratic constraint. -norm minimization problem. In order to deal with this problem with non-convex quadratic constraints and non-convex -norm objective optimization problem, designed -IRW algorithm and -IIRW algorithm. In particular, The IIRW algorithm proposes a new weight update strategy that is not only more adaptable to solving subproblems but also avoids the selection and tuning of regularization parameters. The distributed radar target localization method provided in this embodiment not only significantly enhances positioning robustness and greatly improves target positioning accuracy in low SINR and NLOS anomaly conditions, but also outperforms traditional positioning methods and other robust methods in general Gaussian noise environments with unknown noise variance.

[0058] Finally, numerical simulations verify that the distributed radar target positioning method provided in this embodiment has better positioning performance than existing target positioning methods in low SINR and NLOS abnormal environments.

[0059] The simulation environment is configured to use a distributed MIMO radar system for positioning, which consists of transmitters and The transmitter and receiver are arranged in a system to locate a single target. The positions of the transmitter and receiver are shown in the following table: The target to be detected is located at . Measurement noise It obeys a zero-mean Gaussian distribution with a variance of Assuming that the noise variance is unknown and using the fact that the signal propagation attenuation is proportional to the distance it travels determined by the signal-to-noise ratio level, we define the average SNR as in, is the scale factor, by scaling To generate a preset SNR level. In the performance evaluation, the distributed radar target positioning method provided in this embodiment is compared with the following methods: STWLS-MNS, neural network-based -norm method ( -LPNN) and Norm method ( -LPNN), and Maximum Correntropy Criterion (MCC). The root mean square error is used as the performance evaluation indicator, which is defined as: in, Indicates the Target location in each trial The estimated value of is the number of Monte Carlo trials, which can be set The position of the target to be detected is initialized to obey a random uniform distribution, that is, According to experience, -Regularization parameter in IRW algorithm Set to 0.1. In the numerical simulation, In this case, the algorithm is determined to have converged.

[0060] like Figure 4 and Figure 5 As shown in the low SINR outlier scenario and NLOS scenario, it can be observed that for different signal-to-noise ratios, the The estimator of the value achieves higher estimation accuracy and is therefore more robust in the face of abnormal measurements. In the following simulation experiment, the distributed radar target positioning method provided by this embodiment is used to The value is set to 0.

[0061] like Figure 6 As shown, in the low SINR anomaly case, it is assumed that there are multiple detection targets contaminated by the low SINR anomaly error. Figure 6 (a) and (b) show the performance curves of the RMSE of the target position estimation relative to the abnormal channel SINR level when the SNR is 10 (dB) and 0 (dB) respectively. In the entire low SINR abnormal error level range considered, the proposed method in this embodiment -IIRW algorithm outperforms other existing methods and -IRW algorithm has more accurate position estimation accuracy. And -IRW algorithm is more accurate than the existing method. -The main performance gain of the IIRW algorithm comes from the first few iterations ( =3), so the increased computational cost due to the improvement of positioning accuracy will not be too large.

[0062] like Figure 7 As shown in Figure 1, two simulations are conducted in an NLOS environment: Case 1) NLOS propagation occurs between the first transmitter and the target; Case 2) a transmitter and a receiver are randomly selected, and the NLOS anomaly error is added to the associated measurements. In Case 1, five elements (a full row of data) and in Case 2, nine elements (a full row and a full column of data) are affected by the NLOS error. Assuming SNR = 10 (dB), the simulation results are shown as follows: Figure 7 As shown in (a) and (b), All the compared methods including -IRW are very sensitive to the NLOS anomaly error level (although the MCC method is robust at low NLOS error levels, it performs worse than traditional least squares methods at higher anomaly errors). The RMSE performance of the -IIRW algorithm is insensitive to the overall NLOS anomaly error level in case 1, while in case 2, since the proportion of anomaly measurements in the total measurements is large, -The IIRW algorithm exhibits attenuation at higher levels of anomaly errors.

[0063] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0064] Based on the same inventive concept, embodiments of the present application also provide a distributed radar target positioning device for implementing the aforementioned distributed radar target positioning method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following embodiments of the distributed radar target positioning device can be found in the aforementioned limitations of the distributed radar target positioning method and will not be further elaborated here.

[0065] In one embodiment, Figure 8 As shown, a distributed radar target positioning device 800 is provided, comprising: a matrix construction module 810, a model construction module 820 and a target positioning module 830, wherein: a matrix construction module for constructing a residual matrix according to a bistatic distance measurement matrix, a first distance matrix between a transmitter and a target, and a second distance matrix between a receiver and a target when detecting a target; Model building module for building non-convex Norm minimization problem model; Target positioning module, used to use the first generalized iterative reweighted algorithm to approximate the solution of non-convex The norm minimization problem model is used to obtain the target positioning result.

[0066] In one embodiment, the target positioning module is further used to use the second generalized iterative reweighted algorithm to approximately solve the non-convex The norm minimization problem model is used to obtain the target positioning result.

[0067] Each module in the distributed radar target positioning device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0068] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a distributed radar target positioning method. The display unit of the computer device is used to produce a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0069] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0070] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the aforementioned method embodiment when executing the computer program.

[0071] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the aforementioned method embodiment are implemented.

[0072] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps in the aforementioned method embodiment when executed by a processor.

[0073] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0074] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A distributed radar target positioning method, characterized in that: Applied to a distributed multi-input multi-output radar positioning system; the method includes: In the case of detecting a target, a residual matrix is ​​constructed based on a bistatic distance measurement matrix, a first distance matrix between a transmitter and a target, and a second distance matrix between a receiver and a target; According to the residual matrix, a non-convex Norm minimization problem model; The first generalized iterative reweighted algorithm is used to approximately solve the non-convex The norm minimization problem model is used to obtain the target positioning result.

2. The method according to claim 1, characterized in that The constructing of a residual matrix according to the bistatic distance measurement matrix, the first distance matrix between the transmitter and the target, and the second distance matrix between the receiver and the target includes: in, , , , , is the residual matrix, is the bibasic distance measurement matrix, For the Bistatic range measurements for a transmitter-receiver pair, is the first distance matrix, is the second distance matrix.

3. The method according to claim 2, characterized in that The non-convex matrix is ​​established according to the residual matrix Norm minimization problem models, including: in, Represents the residual matrix norm, is the target location, is the Euclidean distance between the transmitter and the target, is the Euclidean distance between the receiver and the target.

4. The method according to claim 3, characterized in that The non-convex matrix is ​​established according to the residual matrix Norm minimization problem model, also includes: The non-convex Norm The value is set to .

5. The method according to claim 1, wherein The first generalized iterative reweighted algorithm is used to approximately solve the non-convex The norm minimization problem model is used to obtain the target positioning results, including: Initializing algorithm parameters in the first generalized iterative reweighted algorithm, wherein the algorithm parameters include the number of iterations, the position estimate, and the weight; Iteratively solving the weighted minimization problem until the algorithm converges or the number of iterations is greater than or equal to a preset iteration threshold, and using the position estimate value obtained by the last iterative solution of the weighted minimization problem as the target positioning result; wherein, during each iteration, the weight is updated according to the first weight update algorithm; The first weight updating algorithm includes: in, , , For the The weights when solving the weighted minimization problem in the iteration, For the The residual error when iteratively solving the weighted minimization problem, is the regularization parameter, For the Bistatic range measurements for a transmitter-receiver pair, For the The first step in iteratively solving the weighted minimization problem The Euclidean distance between the transmitter and the target, For the The first step in iteratively solving the weighted minimization problem The Euclidean distance between the receiver and the target, For the non-convex Norm value.

6. The method according to claim 5, characterized in that The non-convex matrix is ​​established according to the residual matrix After the norm minimization problem model is established, the method further comprises: The second generalized iterative reweighted algorithm is used to approximately solve the non-convex -norm minimization problem model to obtain the target positioning result.

7. The method according to claim 6, characterized in that The second generalized iterative reweighted algorithm is used to approximately solve the non-convex -norm minimization problem model to obtain target positioning results, including: Initializing algorithm parameters in the second generalized iterative reweighted algorithm, wherein the algorithm parameters include the number of iterations, the position estimate, and the weight; Iteratively solving the weighted minimization problem until the algorithm converges or the number of iterations is greater than or equal to a preset iteration threshold, and using the position estimate value obtained by the last iterative solution of the weighted minimization problem as the target positioning result; wherein, during each iteration, the weight is updated according to the second weight update algorithm; The second weight updating algorithm includes: in, , , The first weight update algorithm The weights when solving the weighted minimization problem in the iteration, The second weight update algorithm The weights when solving the weighted minimization problem in the iteration, For the The residual error when iteratively solving the weighted minimization problem, For the The weight matrix when solving the weighted minimization problem for iterations.

8. A distributed radar target positioning device, characterized in that: Applicable to a distributed multi-input multi-output radar positioning system; the device comprises: a matrix construction module for constructing a residual matrix according to a bistatic distance measurement matrix, a first distance matrix between a transmitter and a target, and a second distance matrix between a receiver and a target when detecting a target; A model building module is used to build a non-convex Norm minimization problem model; The target positioning module is used to approximately solve the non-convex The norm minimization problem model is used to obtain the target positioning result.

9. A communication device comprising a distributed multiple-input multiple-output radar positioning system, a memory, and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the distributed radar target positioning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed radar target positioning method according to any one of claims 1 to 7 are implemented.

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