Self-adaptive fast two-dimensional angle super-resolution method for real aperture phased array radar

Abstract

The invention provides a self-adaptive fast two-dimensional angle super-resolution method for a real aperture phased array radar, and relates to the field of real aperture radar super-resolution imaging. The method comprises the following steps: modeling azimuth and pitching two-dimensional echoes into convolution of a two-dimensional antenna beam along azimuth and pitching sampling sequence and a target reflection function along azimuth and pitching sampling sequence; under a regularization framework, introducing azimuth and pitching two-dimensional sparse constraints of a target, and converting a deconvolution problem into a parameter estimation problem under the regularization framework; and directly solving a two-dimensional inversion problem by adopting an efficient solving method and a two-dimensional rapid iteration threshold shrinkage algorithm. The method solves the problems that a traditional regularization method is low in processing speed and cannot achieve rapid and effective high-resolution imaging. And the real-time performance of the two-dimensional angle super-resolution is greatly improved.

Description

Technical Field

[0001] This document relates to the field of super-resolution imaging with real aperture radar, and in particular to an adaptive fast two-dimensional angular super-resolution method for real aperture phased array radar. Background Technology

[0002] Real aperture radar (SAR) overcomes the limitation of SAR's inability to perform forward-looking imaging, compensates for the blind spots of SAR imaging, and, due to its fast imaging processing, can achieve real-time imaging. It is widely used in civilian and military fields such as material delivery, autonomous navigation of carrier aircraft, and precision guidance of missiles.

[0003] However, when sampling large areas and dense PRFs, the number of azimuth sampling points increases rapidly, making it difficult to achieve fast high-resolution imaging. Furthermore, the angular resolution of a real aperture radar is limited by the size of the radar antenna aperture, making it difficult to deploy large-aperture antennas to meet high-resolution imaging requirements in practical applications.

[0004] Phased array radar can be used for radar imaging, and combining it with compressed sensing theory is a relatively new approach to achieve super-resolution imaging with real aperture radar. In the paper "Y.Quan,R.Zhang,Y.Li,R.Xu,S.Zhu andM.Xing,"Microwave Correlation Forward-Looking Super-Resolution Imaging Based on Compressed Sensing," in IEEE Transactions on Geoscience and Remote Sensing, vol.59, no.10, pp.8326-8337, Oct.2021, doi:10.1109 / TGRS.2020.3047018," a method is proposed that uses phased array radar to form different and random antenna patterns. Then, by combining compressed sensing theory, the target image can be recovered with very few samples, thus overcoming the Rayleigh resolution limitation. Furthermore, the proposed method can achieve at least 5.5 times higher resolution than actual aperture imaging, but this method is still limited by the size and degrees of freedom of the phased array, making it difficult to improve the resolution by more than 10 times.

[0005] Regularization methods are one of the important methods for achieving super-resolution imaging. In the paper "VM Patel, GREasley, D.M. Healy and R. Chellappa, 'Compressed synthetic aperture radar', IEEE J.Sel. Topics Signal Process., vol.4, no.2, pp.244-254, Apr. 2010," a general model is developed by precisely describing the SAR imaging geometry as the observation matrix, and sparse imaging is achieved by solving the L1-norm regularization problem. However, this requires a large amount of computation in constructing the observation matrix and iteratively recovering the scene, which presents certain difficulties in practical applications. In the paper "J.-w. Zou, M.-b. Zhu, X.-p. Li and W. Dong, 'Norm regularization method and its application in radar azimuth super-resolution', 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013), pp.1-4, 2013," different regularization terms are added to improve azimuth resolution. When the regularization term is L2 norm, this is the famous Tikhonov regularization method, but it involves the inversion of large matrices, which has high complexity.

[0006] In summary, while the methods mentioned above can improve the azimuth resolution of real aperture radar to some extent, the performance improvement of single phased array imaging and regularization methods is limited. Furthermore, methods with better imaging results often have slower processing speeds, making it impossible to achieve fast and effective high-resolution imaging.

[0007] Therefore, there is an urgent need for an angle resolution method for real aperture radar to solve the problem that traditional regularization methods are slow and cannot achieve fast and effective high-resolution imaging. Summary of the Invention

[0008] This specification provides an adaptive fast two-dimensional angular super-resolution method for real aperture phased array radar, which is used to solve the problem that traditional regularization methods are slow and cannot achieve fast and effective high-resolution imaging.

[0009] Firstly, this specification provides an adaptive fast two-dimensional angular super-resolution method for real aperture phased array radar, characterized by comprising:

[0010] By using digital beams formed based on array element position phase compensation, echo data is acquired, and a two-dimensional echo model of a real aperture radar is constructed.

[0011] Based on the two-dimensional echo model of the real aperture radar, a regularized objective function is constructed.

[0012] The optimized regularization objective function is obtained by using a two-dimensional fast iterative threshold shrinkage algorithm to perform a near-quadratic approximation on the regularization objective function and taking the minimum value.

[0013] An alternating iterative strategy is used to solve the optimized regularized objective function, and the converged result of the two-dimensional angular super-resolution processing is obtained.

[0014] Traverse all distance cells to obtain 2D super-resolution results for azimuth and elevation.

[0015] Secondly, this specification provides an adaptive fast two-dimensional angular super-resolution device for a real aperture phased array radar, comprising: a two-dimensional echo model construction module, a regularized objective function construction module, an objective function optimization module, a fast iterative solution module, and an azimuth and elevation two-dimensional super-resolution result acquisition module, wherein:

[0016] The two-dimensional echo model construction module is used to acquire echo data and construct a two-dimensional echo model of a real aperture radar by using a digital beam formed based on the phase compensation of array element positions.

[0017] The regularization objective function construction module is used to construct a regularization objective function based on the real aperture radar two-dimensional echo model;

[0018] The objective function optimization module is used to perform a near-end quadratic approximation of the regularization objective function using a two-dimensional fast iterative threshold shrinkage algorithm, and obtain the optimized regularization objective function by taking the minimum value.

[0019] The fast iterative solution module is used to solve the optimized regularized objective function using an alternating iterative strategy to obtain the converged result of the two-dimensional angular super-resolution processing.

[0020] The azimuth and elevation 2D super-resolution result acquisition module is used to traverse all distance cells and acquire the azimuth and elevation 2D super-resolution results.

[0021] Thirdly, this specification also provides a network device, including: a communication interface, a processor, and a memory;

[0022] The processor invokes program instructions from the memory to perform the following actions:

[0023] By using digital beams formed based on array element position phase compensation, echo data is acquired, and a two-dimensional echo model of a real aperture radar is constructed.

[0024] Based on the two-dimensional echo model of the real aperture radar, a regularized objective function is constructed.

[0025] The optimized regularization objective function is obtained by using a two-dimensional fast iterative threshold shrinkage algorithm to perform a near-quadratic approximation on the regularization objective function and taking the minimum value.

[0026] An alternating iterative strategy is used to solve the optimized regularized objective function, and the converged result of the two-dimensional angular super-resolution processing is obtained.

[0027] Traverse all distance cells to obtain 2D super-resolution results for azimuth and elevation.

[0028] The beneficial effects of this invention are as follows:

[0029] This specification presents an adaptive fast two-dimensional angular super-resolution method for real-aperture phased array radar. This method models the azimuth and elevation two-dimensional echoes as the convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences. Under a regularization framework, two-dimensional sparse constraints on the target's azimuth and elevation are introduced, transforming the deconvolution problem into a parameter estimation problem within the regularization framework. An efficient two-dimensional fast iterative threshold shrinkage algorithm is used to directly solve the two-dimensional inverse problem. This method solves the problem that traditional regularization methods are slow and cannot achieve fast and effective high-resolution imaging. Based on a phased array for transmitting and receiving real-aperture radar signals, this method selects a first-norm operator as the regularization constraint. Compared to traditional iterative solution methods, this method uses a two-dimensional fast iterative threshold shrinkage algorithm to solve the objective function. The algorithm's time and space complexity are significantly lower than traditional one-dimensional solution methods, greatly improving the real-time performance of two-dimensional angular super-resolution processing and facilitating rapid resolution of close-range targets by the radar platform. Attached Figure Description

[0030] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings:

[0031] Figure 1 This is a schematic diagram of an adaptive fast two-dimensional angular super-resolution method for a real aperture phased array radar provided in the embodiments of this specification;

[0032] Figure 2 This is a schematic diagram of an antenna array element layout provided in the embodiments of this specification;

[0033] Figure 3 This is a schematic diagram of the radiation pattern of a real aperture antenna provided in the embodiments of this specification;

[0034] Figure 4 This is a schematic diagram of a motion model of an airborne scanning radar provided in the embodiments of this specification;

[0035] Figure 5 This is a schematic diagram of azimuth and elevation two-dimensional digital beamforming provided in the embodiments of this specification;

[0036] Figure 6 This is a schematic diagram of a target original distribution provided in the embodiments of this specification;

[0037] Figure 7 This is a schematic diagram of a raw echo provided in the embodiments of this specification;

[0038] Figure 8 This is a schematic diagram of the reconstruction result of a fast two-dimensional angular super-resolution method for a real aperture phased array provided in the embodiments of this specification;

[0039] Figure 9 This is a schematic diagram of the flow of an adaptive fast two-dimensional angular super-resolution method for a real aperture phased array radar provided in the embodiments of this specification;

[0040] Figure 10 This is a schematic diagram of an adaptive fast two-dimensional angle super-resolution device for a real aperture phased array radar provided in the embodiments of this specification;

[0041] Figure 11 This is a schematic diagram of a network device structure provided in the embodiments of this specification. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments in this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this application without creative effort are within the scope of protection of this document.

[0043] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings. Specific Implementation Example 1:

[0045] This embodiment provides an adaptive fast two-dimensional angular super-resolution method for real aperture phased array radar;

[0046] First, it should be noted that the angular resolution of a real aperture radar is determined by the beamwidth of the antenna, i.e., it is limited by the beamwidth of the transmit and receive antenna patterns. Regularization methods have been widely used in real aperture radar to meet the needs of high-resolution imaging. However, traditional regularization methods consume huge amounts of time and space complexity when dealing with the two-dimensional convolution inversion problem, which seriously restricts the real-time performance of the algorithm and is not conducive to practical applications.

[0047] To address this deficiency, this embodiment proposes an adaptive fast two-dimensional angular super-resolution method based on phased array radar to achieve rapid high-resolution imaging. First, the azimuth and elevation two-dimensional echoes are modeled as convolutions of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences. Second, under a regularization framework, two-dimensional sparse constraints on the target's azimuth and elevation are introduced, transforming the deconvolution problem into a parameter estimation problem under the regularization framework. Finally, the efficient solution method, the Two-Dimensional Fast Iterative Threshold Shrinkage Algorithm (FISTA), is used to directly solve the two-dimensional inverse problem.

[0048] like Figure 1 As shown, the method in this embodiment specifically includes the following steps:

[0049] Step 102: Use the digital beam formed based on the phase compensation of array element position to acquire echo data and construct a two-dimensional echo model of real aperture radar;

[0050] Specifically, one implementation of step 102 can be:

[0051] S21. When the phased array radar transmits continuous wave signals in real aperture mode, it uses a small number of array elements to achieve full coverage of the detection area with a wide beam.

[0052] S22. When receiving echoes, different array elements are weighted and superimposed by weight vectors to concentrate the directional gain of the array elements in one direction, which is equivalent to forming a "beam". The total output of all array elements is the weighted sum of the components of the echo signal on each array element, which is equivalent to the snapshot data formed after the narrow beam antenna scans the detection area at different angles in sequence.

[0053] The echo from the array is guided to different azimuth and elevation angles by the array beamforming. (Multiplied by the corresponding steering vector), the echo data after beamforming is obtained. Here The spacing should be smaller than the beamwidth calculated from the aperture to achieve dense sampling of the spatial domain.

[0054] S23, located in the target airspace with coordinates of The echo signal S(τ,t) of a point target P with scattering intensity σ0 can be expressed in the following form:

[0055]

[0056] in Let f(t) represent the RCS coefficient of the target, f(t) be the antenna pattern modulation function, t be the slow-time variable, rect(·) be the rectangular function, and τ be the fast-time variable of the signal. d It is the echo delay, T rIt is the signal duration, K is the linear frequency modulation coefficient, and f c It is the signal carrier frequency, expjπK(τ-τ) d ) is the Doppler modulation term caused by the platform motion;

[0057] Due to the long operating distance and fast scanning speed, and ignoring the beam distortion and compression effects caused by platform motion, the antenna pattern modulation function is expressed as follows: in This is a two-dimensional antenna radiation pattern;

[0058] S24. Using the transmitted signal, the above echo signal is sequentially subjected to deslant removal and demodulation frequency modulation processing to obtain the target echo after demodulation frequency modulation processing. The echo signal after demodulation frequency modulation processing is expressed as range R, azimuth angle θ, and elevation angle. function in:

[0059]

[0060] in R represents the antenna pattern modulation function, B is the signal bandwidth, c is the speed of light, R represents the range gate, and R0 represents the target range.

[0061] S25. The temporal sequence relationship of the radar antenna beam sweeping across each resolution cell in the target area; the echo acquisition process is represented as a matrix-matrix calculation; based on the antenna beam scanning distance towards the i-th cell, the antenna beam sampling sequence... Construct the azimuth convolution matrix and the elevation convolution matrix;

[0062] The azimuth antenna pattern matrix is ​​as follows:

[0063]

[0064] The elevation antenna pattern matrix is ​​as follows:

[0065]

[0066] After discretization, the echo matrix, target RCS coefficient matrix, and noise matrix are represented by S, σ, and n, respectively, as follows:

[0067]

[0068] The two-dimensional echo model of the real aperture radar refers to:

[0069] The convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences;

[0070] Specifically, S = HσZ T +n;

[0071] Where S represents the echo matrix, H represents the phase-weighted azimuth antenna pattern matrix, Z represents the phase-weighted elevation antenna pattern matrix, σ represents the target RCS coefficient matrix, and n represents the noise matrix.

[0072] It is worth noting that by using digital beamforming to receive echoes, the beam achieves super-resolution and low sidelobe performance, no longer relying on the traditional single elevation and azimuth model. This enables simultaneous detection of targets within the azimuth and elevation coverage area.

[0073] Step 104: Based on the two-dimensional echo model of the real aperture radar, construct a regularized objective function;

[0074] Scene sparsity is common in radar imaging applications, and a first-norm (L1) constraining this sparsity property is often chosen. Within the regularization framework, a first-norm operator is selected as the regularization constraint, where the regularization objective function... Represented as:

[0075]

[0076] in λ is the regularization parameter, where σ represents the target RCS coefficient matrix. ij Let H represent the elements of the target RCS coefficient matrix, H represent the phase-weighted azimuth antenna pattern matrix, Z represent the phase-weighted elevation antenna pattern matrix, and n represent the noise matrix. ij This represents the elements of the noise matrix.

[0077] Based on this, under the regularization framework, the target's azimuth and elevation two-dimensional sparse constraints are introduced, transforming the deconvolution problem into a parameter estimation problem under the regularization framework. This can overcome the noise amplification problem of traditional inversion algorithms, while also taking into account the improvement of resolution in both azimuth and elevation directions.

[0078] Step 106: Using the two-dimensional fast iterative threshold shrinkage algorithm (Fast Iterative Shrinkage-Thresholding Alogrithmetic, FISTA for short), perform a near-quadratic approximation on the regularization objective function and take the minimum value to obtain the optimized regularization objective function;

[0079] Specifically, a near-quadratic approximation is made on the regularization objective function at point z;

[0080] The optimized regularization objective function includes:

[0081]

[0082] in λ is the regularization parameter, L f is the Lipschitz constant, and z represents the starting point of the approximate function in the current iteration;

[0083] Specifically, the two-dimensional fast iterative threshold shrinkage algorithm is used to solve optimization problems:

[0084] minF(σ)=f(σ)+g(σ) (7)

[0085] in g(σ)=‖σ‖1.

[0086] The two-dimensional fast iterative threshold shrinkage algorithm uses a proximal operator for solution, specifically, at point z, it makes a second approximation:

[0087]

[0088] Where tr(·) represents the trace of the matrix, L f This is the Lipschitz constant.

[0089] Ignoring irrelevant constant terms, the minimum value of this function is expressed by the proximal operator, see formula (6);

[0090] Based on this, by approximating and optimizing the objective function, the computational cost of subsequent solutions is reduced.

[0091] Step 108: Using an alternating iterative strategy, solve the optimized regularized objective function to obtain the converged result of the two-dimensional angular super-resolution processing;

[0092] Specifically, step 108 is implemented as follows:

[0093] S81. The initial values ​​of the target scattering coefficients are: σ0 = 0, σ1 = 0, t0 = t1 = 1, where 0 represents the zero matrix of M × N.

[0094] S82. Solve the objective function using an alternating iteration strategy:

[0095]

[0096] Where, soft represents the shrinkage operator, k represents the number of iterations, L is the iteration step size, and λ is the regularization parameter. When convergence is reached, η is considered to be a set threshold, and the σ obtained at this time is considered to be convergence. k+1 This is the result of two-dimensional angular super-resolution processing.

[0097] Based on this, the efficient solution method, the Two-Dimensional Fast Iterative Threshold Shrinkage Algorithm (FISTA), is used to directly solve the two-dimensional inversion problem. By predicting the starting point of the approximate function for the next iteration, the convergence speed can be improved.

[0098] Step 110: Traverse all distance cells to obtain the azimuth and elevation 2D super-resolution results.

[0099] Furthermore, this embodiment uses simulation experiments to demonstrate the effectiveness of the proposed method. All steps and conclusions in this embodiment are verified on the Matlab 2018 simulation platform. The specific implementation steps are as follows: Figure 9 As shown;

[0100] The array element distribution designed in this embodiment is as follows: Figure 2 As shown, in real-aperture imaging, the 2x2 subarray elements in region A1 transmit, and all subarrays in regions B1 and B2 receive. This differs from the ideal element distribution. Considering element position phase compensation, the obtained receiving antenna pattern is as follows. Figure 3 As shown.

[0101] Therefore, for the receiving array in this embodiment, its beamwidth at the imaging boundary (±30°) is approximately Δθ. 0.5 ≈6.51°;

[0102] This embodiment uses an airborne radar azimuth-elevation two-dimensional scanning radar motion model, such as... Figure 4 As shown; the specific parameter values ​​of the airborne platform system are shown in Table 1, and the azimuth and elevation two-dimensional digital beamforming are shown in [reference]. Figure 5 ;

[0103] The azimuth and elevation detection area in this simulation is set to Ω = -30° to 30°.

[0104] The simulation parameters for this embodiment are shown in Table 1. Figure 6 This represents the original scene. Figure 7 This represents the actual echo data. Figure 8 The super-resolution results of this invention are shown. It can be seen that the algorithm proposed in this embodiment can accurately image the target, verifying the effectiveness of the proposed algorithm.

[0105] Table 1: Simulation Parameter Table

[0106]

[0107]

[0108] In summary, this embodiment models the azimuth and elevation two-dimensional echoes as the convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences. Under a regularization framework, two-dimensional sparse constraints on the target's azimuth and elevation are introduced, transforming the deconvolution problem into a parameter estimation problem within the regularization framework. An efficient solution method, the two-dimensional fast iterative threshold shrinkage algorithm, is used to directly solve the two-dimensional inverse problem. This method solves the problem that traditional regularization methods are slow and cannot achieve fast and effective high-resolution imaging. Based on a phased array for transmitting and receiving real-aperture radar signals, this method selects a first-norm operator as the regularization constraint. Compared to traditional iterative solution methods, this method uses a two-dimensional fast iterative threshold shrinkage algorithm to solve the objective function. The algorithm's time and space complexity are far lower than traditional one-dimensional solution methods, greatly improving the real-time performance of two-dimensional angular super-resolution processing and facilitating rapid resolution of close-range targets by the radar platform. Specific Implementation Example 2:

[0110] This embodiment provides an adaptive fast two-dimensional angular super-resolution device for a real aperture phased array radar. See [link to documentation]. Figure 10 It includes: a two-dimensional echo model construction module 1001, a regularized objective function construction module 1002, an objective function optimization module 1003, a fast iterative solution module 1004, and an azimuth and elevation two-dimensional super-resolution result acquisition module 1005, wherein:

[0111] The two-dimensional echo model construction module 1001 is used to acquire echo data and construct a two-dimensional echo model of a real aperture radar by using a digital beam formed based on the phase compensation of array element positions.

[0112] The regularization objective function construction module 1002 is used to construct a regularization objective function based on the real aperture radar two-dimensional echo model;

[0113] The objective function optimization module 1003 is used to perform a near-end quadratic approximation of the regularization objective function using a two-dimensional fast iterative threshold shrinkage algorithm, and obtain the optimized regularization objective function by taking the minimum value.

[0114] The fast iterative solution module 1004 is used to solve the optimized regularized objective function using an alternating iterative strategy to obtain the converged result of the two-dimensional angular super-resolution processing.

[0115] The azimuth and elevation two-dimensional super-resolution result acquisition module 1005 is used to traverse all distance cells and acquire the azimuth and elevation two-dimensional super-resolution results.

[0116] Optionally, the two-dimensional echo model construction module 1001 is specifically used for:

[0117] When a phased array radar transmits a continuous wave signal in real aperture mode, it uses a small number of array elements to achieve full coverage of the detection area with a wide beam.

[0118] When receiving the echo, different array elements are weighted and superimposed by weight vectors to concentrate the directional gain of the array elements in one direction. The total output of all array elements is the weighted sum of the components of the echo signal on each array element, which is equivalent to the snapshot data formed by the narrow beam antenna scanning the detection area at different angles in sequence.

[0119] Located in the target airspace with coordinates The echo signal S(τ,t) of a point target P with scattering intensity σ0 can be expressed in the following form:

[0120]

[0121] in Let f(t) represent the RCS coefficient of the target, f(t) be the antenna pattern modulation function, t be the slow-time variable, rect(·) be the rectangular function, and τ be the fast-time variable of the signal. d It is the echo delay, T r It is the signal duration, K is the linear frequency modulation coefficient, and f c It is the signal carrier frequency, expjπK(τ-τ) d ) is the Doppler modulation term caused by the platform motion;

[0122] The antenna pattern modulation function is expressed as follows: in This is a two-dimensional antenna radiation pattern;

[0123] The transmitted signal is used to sequentially perform deslant removal and demodulation frequency modulation processing on the above echo signal to obtain the demodulated frequency modulation target echo. The demodulated frequency modulation echo signal is then expressed as range R, azimuth θ, and elevation angle. function

[0124]

[0125] in R represents the antenna pattern modulation function, B is the signal bandwidth, c is the speed of light, R represents the range gate, and R0 represents the target range.

[0126] The temporal sequence relationship of the radar antenna beam sweeping across each resolution cell in the target area is used to represent the echo acquisition process as a matrix-matrix calculation; based on the antenna beam scanning distance towards the i-th cell, the antenna beam sampling sequence... Construct the azimuth convolution matrix and the elevation convolution matrix;

[0127] The azimuth antenna pattern matrix is ​​as follows:

[0128]

[0129] The elevation antenna pattern matrix is ​​as follows:

[0130]

[0131] Optionally, the two-dimensional echo model of the real aperture radar refers to:

[0132] The convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences;

[0133] Specifically, S = HσZ T +n;

[0134] Where S represents the echo matrix, H represents the phase-weighted azimuth antenna pattern matrix, Z represents the phase-weighted elevation antenna pattern matrix, σ represents the target RCS coefficient matrix, and n represents the noise matrix.

[0135] Optionally, the regularization objective function Represented as:

[0136]

[0137] in λ is the regularization parameter; where σ represents the target RCS coefficient matrix. ij Represents each element of the target RCS coefficient matrix, n ij This represents the elements of the noise matrix.

[0138] Optionally, the optimized regularization objective function includes:

[0139]

[0140] in λ is the regularization parameter; L f is the Lipschitz constant, and z represents the starting point of the approximate function in the current iteration.

[0141] In summary, this embodiment models the azimuth and elevation two-dimensional echoes as the convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences. Under a regularization framework, two-dimensional sparse constraints on the target's azimuth and elevation are introduced, transforming the deconvolution problem into a parameter estimation problem within the regularization framework. An efficient solution method, the two-dimensional fast iterative threshold shrinkage algorithm, is used to directly solve the two-dimensional inverse problem. This method solves the problem that traditional regularization methods are slow and cannot achieve fast and effective high-resolution imaging. Based on a phased array for transmitting and receiving real-aperture radar signals, this method selects a first-norm operator as the regularization constraint. Compared to traditional iterative solution methods, this method uses a two-dimensional fast iterative threshold shrinkage algorithm to solve the objective function. The algorithm's time and space complexity are far lower than traditional one-dimensional solution methods, greatly improving the real-time performance of two-dimensional angular super-resolution processing and facilitating rapid resolution of close-range targets by the radar platform. Specific Implementation Example 3:

[0143] This specification also provides an electronic device, see [link to documentation]. Figure 11 The electronic device is capable of implementing the details of the method described in the above embodiments and achieving the same effect. For example... Figure 11 As shown, the electronic device 1100 includes: a processor 1101, a transceiver 1102, a memory 1103, a user interface 1104, and a bus interface, wherein:

[0144] In this embodiment of the specification, the electronic device 1100 further includes: a computer program stored in the memory 1103 and executable by the processor 1101, the computer program being executed by the processor 1101 to perform the following steps:

[0145] By using digital beams formed based on array element position phase compensation, echo data is acquired, and a two-dimensional echo model of a real aperture radar is constructed.

[0146] Based on the two-dimensional echo model of the real aperture radar, a regularized objective function is constructed.

[0147] The optimized regularization objective function is obtained by using a two-dimensional fast iterative threshold shrinkage algorithm to perform a near-quadratic approximation on the regularization objective function and taking the minimum value.

[0148] An alternating iterative strategy is used to solve the optimized regularized objective function, and the converged result of the two-dimensional angular super-resolution processing is obtained.

[0149] Traverse all distance cells to obtain 2D super-resolution results for azimuth and elevation.

[0150] exist Figure 11In this document, the bus framework may include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 1101 and memory represented by memory 1103 together. The bus framework may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be further described here. The bus interface provides an interface. The transceiver 1102 may be multiple elements, including a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium. For different user equipment, the user interface 1104 may also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0151] The processor 1101 features an amplitude management bus architecture and typical processing. The memory 1103 can store data used by the processor 1101 during operation. Optionally, when a computer program is executed by the processor 1101, it can also perform the following steps:

[0152] Optionally, the step of using a digital beam formed based on array element position phase compensation to acquire echo data and construct a two-dimensional echo model of a real aperture radar includes:

[0153] When a phased array radar transmits a continuous wave signal in real aperture mode, it uses a small number of array elements to achieve full coverage of the detection area with a wide beam.

[0154] When receiving the echo, different array elements are weighted and superimposed by weight vectors to concentrate the directional gain of the array elements in one direction. The total output of all array elements is the weighted sum of the components of the echo signal on each array element, which is equivalent to the snapshot data formed by the narrow beam antenna scanning the detection area at different angles in sequence.

[0155] Located in the target airspace with coordinates The echo signal S(τ,t) of a point target P with scattering intensity σ0 can be expressed in the following form:

[0156]

[0157] in Let f(t) represent the RCS coefficient of the target, f(t) be the antenna pattern modulation function, t be the slow-time variable, rect(·) be the rectangular function, and τ be the fast-time variable of the signal. d It is the echo delay, T r It is the signal duration, K is the linear frequency modulation coefficient, and f c It is the signal carrier frequency, expjπK(τ-τ) d ) is the Doppler modulation term caused by the platform motion;

[0158] The antenna pattern modulation function is expressed as follows:

[0159] The transmitted signal is used to sequentially perform deslant removal and demodulation frequency modulation processing on the above echo signal to obtain the demodulated frequency modulation target echo. The demodulated frequency modulation echo signal is then expressed as range R, azimuth θ, and elevation angle. function

[0160]

[0161] in R represents the antenna pattern modulation function, B is the signal bandwidth, c is the speed of light, R represents the range gate, and R0 represents the target range.

[0162] The temporal sequence relationship of the radar antenna beam sweeping across each resolution cell in the target area is used to represent the echo acquisition process as a matrix-matrix calculation; based on the antenna beam scanning distance towards the i-th cell, the antenna beam sampling sequence... Construct the azimuth convolution matrix and the elevation convolution matrix;

[0163] The azimuth antenna pattern matrix is ​​as follows:

[0164]

[0165] The elevation antenna pattern matrix is ​​as follows:

[0166]

[0167] Optionally, the two-dimensional echo model of the real aperture radar refers to:

[0168] The convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences;

[0169] Specifically, S = HσZ T +n;

[0170] Where S represents the echo matrix, H represents the phase-weighted azimuth antenna pattern matrix, Z represents the phase-weighted elevation antenna pattern matrix, σ represents the target RCS coefficient matrix, and n represents the noise matrix.

[0171] Optionally, the regularization objective function is expressed as:

[0172]

[0173] in λ is the regularization parameter; where σ represents the target RCS coefficient matrix. ij Represents each element of the target RCS coefficient matrix, nij This represents the elements of the noise matrix.

[0174] Optionally, the optimized regularization objective function includes:

[0175]

[0176] in λ is the regularization parameter; L f is the Lipschitz constant, and z represents the starting point of the approximate function in the current iteration.

[0177] In summary, this embodiment models the azimuth and elevation two-dimensional echoes as the convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences. Under a regularization framework, two-dimensional sparse constraints on the target's azimuth and elevation are introduced, transforming the deconvolution problem into a parameter estimation problem within the regularization framework. An efficient solution method, the two-dimensional fast iterative threshold shrinkage algorithm, is used to directly solve the two-dimensional inverse problem. This method solves the problem that traditional regularization methods are slow and cannot achieve fast and effective high-resolution imaging. Based on a phased array for transmitting and receiving real-aperture radar signals, this method selects a first-norm operator as the regularization constraint. Compared to traditional iterative solution methods, this method uses a two-dimensional fast iterative threshold shrinkage algorithm to solve the objective function. The algorithm's time and space complexity are far lower than traditional one-dimensional solution methods, greatly improving the real-time performance of two-dimensional angular super-resolution processing and facilitating rapid resolution of close-range targets by the radar platform.

[0178] The above description is merely a preferred embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

Claims

1. An adaptive fast two-dimensional angular super-resolution method for real aperture phased array radar, characterized in that, include: By using digital beams formed based on array element position phase compensation, echo data is acquired, and a two-dimensional echo model of a real aperture radar is constructed. Based on the two-dimensional echo model of the real aperture radar, a regularized objective function is constructed. The optimized regularization objective function is obtained by using a two-dimensional fast iterative threshold shrinkage algorithm to perform a near-quadratic approximation on the regularization objective function and taking the minimum value. An alternating iterative strategy is used to solve the optimized regularized objective function, and the converged result of the two-dimensional angular super-resolution processing is obtained. Traverse all distance cells to obtain 2D super-resolution results for azimuth and elevation.

2. The method according to claim 1, characterized in that, The process of acquiring echo data and constructing a two-dimensional echo model for a real aperture radar using a digital beam formed based on array element position phase compensation includes: When a phased array radar transmits a continuous wave signal in real aperture mode, it uses a small number of array elements to achieve full coverage of the detection area with a wide beam. When receiving the echo, different array elements are weighted and superimposed by weight vectors to concentrate the directional gain of the array elements in one direction. The total output of all array elements is the weighted sum of the components of the echo signal on each array element, which is equivalent to the snapshot data formed by the narrow beam antenna scanning the detection area at different angles in sequence. Located in the target airspace with coordinates The echo signal S(τ,t) of a point target P with scattering intensity σ0 can be expressed in the following form: in Let f(t) represent the RCS coefficient of the target, f(t) be the antenna pattern modulation function, t be the slow-time variable, rect(·) be the rectangular function, and τ be the fast-time variable of the signal. d It is the echo delay, T r It is the signal duration, K is the linear frequency modulation coefficient, and f c It is the signal carrier frequency, expjπK(τ-τ) d ) is the Doppler modulation term caused by the platform motion; The transmitted signal is used to sequentially perform deslant removal and demodulation frequency modulation processing on the above echo signal to obtain the demodulated frequency modulation target echo. The demodulated frequency modulation echo signal is then expressed as range R, azimuth θ, and elevation angle. function in R represents the antenna pattern modulation function, B is the signal bandwidth, c is the speed of light, R represents the range gate, and R0 represents the target range. The temporal sequence relationship of the radar antenna beam sweeping across each resolution cell in the target area is used to represent the echo acquisition process as a matrix-matrix calculation; based on the antenna beam scanning distance towards the i-th cell, the antenna beam sampling sequence... Construct the azimuth convolution matrix and the elevation convolution matrix; The azimuth antenna pattern matrix is ​​as follows: The elevation antenna pattern matrix is ​​as follows:

3. The method according to claim 2, characterized in that, The real aperture radar two-dimensional echo model refers to the convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences. Specifically, S = HσZ T +n; Where S represents the echo matrix, H represents the phase-weighted azimuth antenna pattern matrix, Z represents the phase-weighted elevation antenna pattern matrix, σ represents the target RCS coefficient matrix, and n represents the noise matrix.

4. The method according to claim 3, characterized in that, The regularization objective function Represented as: in λ is the regularization parameter; where σ represents the target RCS coefficient matrix. ij Represents each element of the target RCS coefficient matrix, n ij This represents the elements of the noise matrix.

5. The method according to claim 4, characterized in that, The optimized regularization objective function includes: in λ is the regularization parameter; L f is the Lipschitz constant, and z represents the starting point of the approximate function in the current iteration.

6. An adaptive fast two-dimensional angular super-resolution device for a real aperture phased array radar, applied to the method of any one of claims 1 to 5, characterized in that, include: The system includes a 2D echo model construction module, a regularized objective function construction module, an objective function optimization module, a fast iterative solution module, and a 2D super-resolution result acquisition module for azimuth and elevation. The two-dimensional echo model construction module is used to acquire echo data and construct a two-dimensional echo model of a real aperture radar by using a digital beam formed based on the phase compensation of array element positions. The regularization objective function construction module is used to construct a regularization objective function based on the real aperture radar two-dimensional echo model; The objective function optimization module is used to perform a near-end quadratic approximation of the regularization objective function using a two-dimensional fast iterative threshold shrinkage algorithm, and obtain the optimized regularization objective function by taking the minimum value. The fast iterative solution module is used to solve the optimized regularized objective function using an alternating iterative strategy to obtain the converged result of the two-dimensional angular super-resolution processing. The azimuth and elevation 2D super-resolution result acquisition module is used to traverse all distance cells and acquire the azimuth and elevation 2D super-resolution results.

7. The apparatus according to claim 6, characterized in that, The two-dimensional echo model of the real aperture radar refers to: The convolution of the two-dimensional antenna beam along the azimuth and elevation sampling sequences with the target reflection function along the azimuth and elevation sampling sequences; Specifically, S = HσZ T +n; where S represents the echo matrix, H represents the phase-weighted azimuth antenna pattern matrix, Z represents the phase-weighted elevation antenna pattern matrix; σ represents the target RCS coefficient matrix; and n represents the noise matrix.

8. The apparatus according to claim 7, characterized in that, The regularization objective function Represented as: in λ is the regularization parameter; where σ represents the target RCS coefficient matrix. ij Represents each element of the target RCS coefficient matrix, n ij This represents the elements of the noise matrix.

9. The apparatus according to claim 8, characterized in that, The optimized regularization objective function includes: in λ is the regularization parameter, L f is the Lipschitz constant, and z represents the starting point of the approximate function in the current iteration.

10. A network device, characterized in that, include: Communication interface, processor, and memory; The processor invokes program instructions from the memory to perform the following actions: By using digital beams formed based on array element position phase compensation, echo data is acquired, and a two-dimensional echo model of a real aperture radar is constructed. Based on the two-dimensional echo model of the real aperture radar, a regularized objective function is constructed. The optimized regularization objective function is obtained by using a two-dimensional fast iterative threshold shrinkage algorithm to perform a near-quadratic approximation on the regularization objective function and taking the minimum value. An alternating iterative strategy is used to solve the optimized regularized objective function, and the converged result of the two-dimensional angular super-resolution processing is obtained. Traverse all distance cells to obtain 2D super-resolution results for azimuth and elevation.

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