CN-120314877-B - MIMO radar mutual coupling error and DOA joint estimation method based on energy valley-compressed sensing

CN120314877BCN 120314877 BCN120314877 BCN 120314877BCN-120314877-B

Abstract

The invention discloses a method for jointly estimating MIMO radar mutual coupling errors and DOA based on energy valley-compressed sensing. According to the method, under a compressed sensing design framework, the problem of poor accuracy of target angle identification caused by mutual coupling errors of an array model of the MIMO radar is solved, different disturbance strategies are introduced into an EVO algorithm, and weighting and subspace filtering operations are introduced into a CS signal reconstruction process, so that the EVO-CS algorithm is improved, the algorithm can obtain a global optimal solution to the greatest extent, meanwhile, the robustness of the algorithm under severe conditions such as low signal-to-noise ratio is enhanced, the applicability of the compressed sensing algorithm in the MIMO radar is widened, and the solving capability of the compressed sensing algorithm in realizing target angle positioning in the MIMO radar scene is improved and improved. The example shows that the method can realize more accurate mutual coupling error coefficient and DOA estimation result, has stronger global optimizing capability and highest estimation accuracy, and effectively enhances the detection performance of the MIMO radar system for identifying the space target.

Inventors

SUN LU
WANG QINGBO
HAO JIE
CHEN BING

Assignees

南京航空航天大学

Dates

Publication Date: 20260512
Application Date: 20250412

Claims (9)

1. The method for jointly estimating the mutual coupling error and DOA of the MIMO radar based on energy valley-compressed sensing is characterized by comprising the following implementation steps: S1, constructing an array element arrangement mode of a transmitting array and a receiving array of the MIMO radar, wherein array elements in the transmitting array are arranged according to a nested array model, and the receiving array adopts a relatively sparse uniform array; S2, respectively modeling a guide vector and a guide matrix corresponding to the transmitting array and the receiving array according to the array element positions of the transmitting array and the receiving array, so as to establish a mathematical model of target echo data received by the MIMO radar; s3, constructing a mutual coupling error matrix corresponding to the transmitting array by taking the mutual coupling error coefficient as an object according to the position relation of each array element in the transmitting array; S4, determining mathematical expression of coupling coefficients according to the mutual coupling error matrix of the transmitting array; S5, constructing array receiving data of the MIMO radar under the influence of the mutual coupling error; S6, constructing a covariance matrix of the received data of the MIMO radar array; s7, utilizing vectorization operation of the matrix to obtain virtual array receiving data corresponding to a virtual array formed by the MIMO radar; s8, designing a dictionary matrix by utilizing sparsity of the target signal in a space angle domain; S9, under the condition of sparsity, constructing virtual array receiving data; s10, under a CS framework, constructing a joint optimization problem of mutual coupling error coefficient and DOA estimation under a MIMO radar scene, wherein the mathematical expression is as follows: In the formula, Representing the coefficient of mutual coupling error, Representation vectorization The vector of the back-up vector is calculated, Representing the signal's autocorrelation matrix, Representing the coupling coefficient corresponding to the mutual coupling error of the transmit array, Representation of The norm of the sample is calculated, Representation of The norm of the sample is calculated, Representing the signal vectors corresponding to the dictionary matrix, And Respectively represent the first of the transmitting arrays And (d) The positions of the array elements are arranged in the same way, Is a positive number, and the number of the positive numbers is a positive number, In the form of a dictionary matrix, Is a virtual reception vector corresponding to the virtual array of the MIMO radar, Representing the maximum interaction interval of the mutual coupling errors; S11, solving the joint optimization problem in the step S10 by using a modified EVO-CS algorithm, wherein the algorithm is based on the input maximum iteration number and roughly estimated signal angle Calculating an output cross-coupling error estimation matrix, and calculating a DOA estimation value of the signal, wherein the calculating comprises the following steps of: 1) Calculating and estimating mutual coupling error coefficient Fixing means , The iteration times are the iteration times, the joint optimization problem is simplified, and the improved EVO algorithm is utilized to solve to obtain the mutual coupling error coefficient: constructing a cross-coupling error coefficient matrix based on the estimated cross-coupling error coefficients And compensating for virtual reception data of the MIMO radar ; 3) Estimating signal angle Fixed cross-coupling error matrix The simplified joint optimization problem is as follows, and the improved WSOMP algorithm is utilized to solve to obtain an estimated value of the target angle; 4) Updating virtual reception data of the MIMO radar: 。
2. The method for jointly estimating the mutual coupling error and the DOA of the MIMO radar according to claim 1, wherein the step S1 specifically comprises the following steps: sharing in a transmit array of a MIMO radar The array elements are arranged in a nested array form, wherein the subarray 1 is provided with Each array element has the interval of The location of each array element is described as Sub-array 2 has Each array element has the interval of And the distance between the two subarrays is , The array element positions of the transmitting array are expressed as ; The receiving array adopts a sparse uniform array layout mode, and comprises Each array element has the interval of The receive array is represented as 。
3. The method for jointly estimating the mutual coupling error and the DOA of the MIMO radar according to claim 2, wherein the specific process of the step S2 comprises the following steps: according to the steering matrix of the transmitting matrix and the receiving matrix, a mathematical model of the MIMO radar receiving data after matched filtering is established, and the mathematical model is as follows: Wherein, the Representing the steering vector of the transmit array, Representing the steering vector of the receive array, Representing the steering matrix of the joint array, The signal vector is represented as a vector of signals, Representing the noise vector and the noise vector is represented, The number of the incident signals is represented, Representing the kronecker product of the two, Represents the KR product.
4. The method for jointly estimating the mutual coupling error and the DOA of the MIMO radar according to claim 3, wherein the coupling coefficient corresponding to the mutual coupling error of the transmitting array determined in the step S4 is: Wherein, the Representing the F norm; representing the operation of taking diagonal elements of the matrix, Representing the cross-coupling error matrix in the transmit array.
5. The method for jointly estimating the mutual coupling error and the DOA of the MIMO radar according to claim 4, wherein the constructing the array receiving data of the MIMO radar under the influence of the mutual coupling error in the step S5 is as follows: Wherein, the Representing a joint steering matrix affected by the mutual coupling error; representing joint steering vectors affected by cross coupling errors.
6. The method for jointly estimating the mutual coupling error and the DOA of the MIMO radar according to claim 5, wherein the step S6 is to construct a covariance matrix of the array receiving data of the MIMO radar as follows: Wherein, the Representing a signal autocorrelation matrix; Representing noise power; Representing dimensions as Is a matrix of units of (a); representing a desired operation; Representing the conjugate transpose operation.
7. The method for jointly estimating the mutual coupling error and the DOA of the MIMO radar according to claim 6, wherein the step S7 is to construct virtual received data corresponding to the virtual array of the MIMO radar: Wherein, the Representing a joint virtual array steering matrix affected by the cross coupling error; representing a joint virtual array steering vector affected by the cross coupling error; Representation vectorization A post vector; Representation vectorization The vector of the back-up vector is calculated, Is shown in the first Column vectors with 1 in each position and 0 in the rest; representing a vectorization operation; Representing a conjugation operation.
8. The method for jointly estimating the mutual coupling error and the DOA of the MIMO radar according to claim 7, wherein the step S8 is to design a dictionary matrix by utilizing the sparsity of the target signal in the spatial angle domain, and the dictionary matrix is as follows: Wherein, the Column vectors representing dictionary matrices, an ; Represents grid points that divide the spatial domain angle, and 。
9. The method for jointly estimating the mutual coupling error and the DOA of the MIMO radar according to claim 8, wherein the step S9 constructs ideal virtual array received data under the condition of sparsity as follows: Wherein, the Representing a signal vector corresponding to a dictionary matrix and having values only at locations of true signals, values of 0 at the remaining locations, and sparsity of Is a characteristic of (a).

Description

MIMO radar mutual coupling error and DOA joint estimation method based on energy valley-compressed sensing Technical Field The invention relates to a technology for jointly estimating a mutual coupling error coefficient and DOA by using an improved energy valley-compressed sensing method under a MIMO radar, and belongs to the field of radar array signal processing. Background As a popular new radar mode, a multiple input multiple output (Multiple Input Multiple Output, MIMO) radar, which processes target echo signals using multiple transmit and receive antennas, has been widely used in various fields such as autopilot, vital sign detection, ocean sonar, and the like. Compared to a single receive-only array, MIMO radar expands the virtual array aperture by virtue of the sum-of-array concept, enabling more accurate estimation of direction of arrival (Direction of Arrival, DOA). However, the transceiver array of the conventional MIMO radar generally uses a uniform linear array structure, and this dense array element layout mode may cause serious mutual coupling effect and limited aperture length, which affects the subsequent target estimation result. In addition, excessive physical array elements can aggravate the hardware cost and processing complexity of the system, and are unfavorable for real-time processing of engineering. Therefore, the combination of the MIMO radar and the sparse transceiver array has important research significance for improving the performance of the radar system. In order to break through the limitations of the nyquist sampling theorem, compressed sensing (Compressed Sensing, CS) theory has evolved. The method utilizes the sparsity or compressibility of the signal, and realizes signal reconstruction and parameter super-resolution estimation by means of few-snapshot and even single-snapshot signals. CS theory does not care about the sampled value of the signal itself, but rather focuses on the information contained in the signal, and therefore can acquire the signal at a rate well below nyquist's sampling law. In MIMO radar applications, the target incoming signal received by the receiver is limited to only a few specific angles, and no signal is incident in most angular directions in the whole space, so the target signal has sparsity in space. Therefore, the CS technology is of great development significance in achieving accurate target angle estimation. The radar generally assumes that the array steering matrix is not affected by any effect when performing an angle estimation of the target echo signal. However, in practical applications, the mutual coupling effect between array elements is often unavoidable. The adjacent array elements cause the distortion of the radiation pattern due to electromagnetic interaction, so that the radiation characteristic of the antenna is directly affected, and the angle estimation precision and the algorithm stability are reduced. Therefore, how to design an optimization algorithm not only can accurately compensate the mutual coupling error coefficient among array elements, but also can obtain accurate target angle estimation, and becomes a key problem to be solved by the MIMO radar system. The existing mutual coupling error coefficient and angle estimation methods, such as a beam forming algorithm, a subspace algorithm, a deep learning algorithm and the like, are limited by the mutual coupling error coefficient and the angle estimation method. The beam forming algorithm is simple but is limited by Rayleigh limit, the resolution is low, the signal sources with close angles cannot be resolved, the subspace algorithm has high resolution but is sensitive to model assumptions, and the deep learning algorithm has strong adaptability but depends on data. In contrast, CS, by utilizing signal sparsity, realizes high-precision angle estimation while reducing hardware burden, and becomes an ideal choice in resource-constrained scenarios. But in CS framework the design of the dictionary matrix and reconstruction algorithm has a crucial impact on the final signal recovery result. It not only needs to meet strict mathematical conditions, but also gives consideration to computing efficiency, hardware realization and noise immunity. Reasonable design can realize efficient and robust signal recovery under the condition of using a small amount of sampling data. In addition, the orthogonal matching pursuit (Orthogonal Matching Pursuit, OMP) algorithm is still limited in terms of computational efficiency, noise robustness, grid dependence, etc. in the DOA estimation. Therefore, the invention provides a MIMO radar mutual coupling error and DOA joint estimation method based on energy valley (ENERGY VALLEY optimizer, EVO) -CS (EVO-CS), which firstly designs an array mode of a novel sparse receiving-transmitting array, and obtains accurate mutual coupling error coefficient and signal angle by utilizing an improved EVO-CS method under the circulation of rotation iteration t