CN-121995332-A - Self-adaptive rapid two-dimensional angle super-resolution method of real aperture phased array radar

CN121995332ACN 121995332 ACN121995332 ACN 121995332ACN-121995332-A

Abstract

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

Inventors

HAN RUI
LI YUZHAO
LIU WEIZONG
ZHANG YIN
Mao deqing
HUANG YULIN
ZHANG YONGCHAO

Assignees

北京遥感设备研究所

Dates

Publication Date: 20260508
Application Date: 20251230

Claims (10)

1. The self-adaptive rapid two-dimensional angle super-resolution method of the real aperture phased array radar is characterized by comprising the following steps of: Acquiring echo data by using a digital wave beam formed based on array element position phase compensation, and constructing a real aperture radar two-dimensional echo model; Constructing a regularized target function based on the real aperture radar two-dimensional echo model; Performing near-end secondary approximation on the regularized target function by using a two-dimensional rapid iterative threshold contraction algorithm, and taking a regularized target function with the minimum value after optimization; Adopting an alternate iteration strategy to solve the regularized objective function after optimization to obtain a result of the two-dimensional angle super-resolution processing after convergence; Traversing all the distance units to obtain an azimuth pitching two-dimensional super-resolution result.
2. The method of claim 1, wherein the acquiring echo data using digital beams formed based on array element position phase compensation, and constructing a real aperture radar two-dimensional echo model, comprises: when the phased array radar transmits continuous wave signals in a real aperture mode, a small number of array elements are used, and all coverage of a detection area is realized by wide beams; When receiving the echo, different array elements gather the direction gain received by the array elements in one direction through weight vector weighted superposition, and the total output of all the array elements is the weighted sum of the components of the echo signals on each array element, which is equivalent to snapshot data formed after the narrow beam antenna scans the detection areas under different angles in sequence; Coordinates in the target airspace are The echo signal S (τ, t) of the point target P of the scattering intensity σ 0 is expressed as follows: Wherein the method comprises the steps of RCS coefficient representing target, f (T) is antenna pattern modulation function, T is slow time variable, rect (·) is rectangular function, τ is signal fast time variable, τ d is echo delay, T r is signal time width, K is chirp coefficient, f c is signal carrier frequency, expj pi K (τ - τ d ) is Doppler modulation term caused by platform motion; Sequentially performing de-skew and de-linearity tone processing on the echo signals by using the transmitting signals to obtain target echoes after the de-linearity tone processing, and representing the echo signals after the de-linearity tone processing as a distance R, an azimuth angle theta and a pitch angle Is a function of (2) Wherein the method comprises the steps of Representing an antenna pattern modulation function, B being a signal bandwidth, c being a speed of light, R representing a distance gate, R 0 representing a target distance; The time sequential relation of each resolution unit of the radar antenna beam scanning target area represents the echo acquisition process into a matrix-matrix calculation form, and the antenna beam sampling sequence is when the ith unit is directed according to the antenna beam scanning distance Constructing an azimuth convolution matrix and a pitching convolution matrix; The azimuth antenna pattern matrix is as follows: The pitch antenna pattern matrix is:
3. The method of claim 2, wherein the real aperture radar two-dimensional echo model refers to convolution of a two-dimensional antenna beam along azimuth and elevation sampling sequences with a target reflection function along azimuth and elevation sampling sequences; In particular, the method comprises the steps of, s=hσz T +n; wherein S represents an echo matrix, H represents a phase weighted azimuth antenna pattern matrix, Z represents a phase weighted elevation antenna pattern matrix, sigma represents a target RCS coefficient matrix, and n represents a noise matrix.
4. A method according to claim 3, characterized in that the regularized objective function Expressed as: Wherein the method comprises the steps of Λ is a regularization parameter, where σ represents the target RCS coefficient matrix, σ ij represents the elements of the target RCS coefficient matrix, and n ij represents the elements of the noise matrix.
5. The method of claim 4, wherein the optimized regularized objective function comprises: Wherein the method comprises the steps of Lambda is a regularization parameter, L f is a Lipschitz constant, and z represents the approximate function starting point of the current iteration.
6. The self-adaptive rapid two-dimensional angle super-resolution device of the real aperture phased array radar, which is applied to the method of any one of claims 1 to 5, is characterized by comprising a two-dimensional echo model construction module, a regularized objective function construction module, an objective function optimization module, a rapid iteration solving module and an azimuth pitching two-dimensional super-resolution result acquisition module, wherein: The two-dimensional echo model construction module is used for acquiring echo data by utilizing a digital wave beam formed based on array element position phase compensation and constructing a real aperture radar two-dimensional echo model; The regularization objective function construction module is used for constructing a regularization objective function based on the real aperture radar two-dimensional echo model; The target function optimization module is used for performing near-end secondary approximation on the regularized target function by utilizing a two-dimensional rapid iteration threshold contraction algorithm, and taking the regularized target function after optimization by a minimum value; the rapid iteration solving module is used for solving the regularized objective function after optimization by adopting an alternate iteration strategy to obtain a result of the two-dimensional angle super-resolution processing after convergence; the azimuth pitching two-dimensional super-resolution result acquisition module is used for traversing all the distance units and acquiring azimuth pitching two-dimensional super-resolution results.
7. The apparatus of claim 6, wherein the real aperture radar two-dimensional echo model is: Convolution of the azimuth and elevation sampling sequences of the two-dimensional antenna beam and the azimuth and elevation sampling sequences of the target reflection function; 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 of claim 7, wherein the regularized objective function Expressed as: Wherein the method comprises the steps of Λ is a regularization parameter, where σ represents the target RCS coefficient matrix, σ ij represents the elements of the target RCS coefficient matrix, and n ij represents the elements of the noise matrix.
9. The apparatus of claim 8, wherein the optimized regularized objective function comprises: Wherein the method comprises the steps of Λ is the regularization parameter, L f is the Lipschitz constant, and z represents the approximate function starting point for the current iteration.
10. A network device is characterized by comprising a communication interface, a processor and a memory; The processor invokes program instructions in the memory for performing the following actions: Acquiring echo data by using a digital wave beam formed based on array element position phase compensation, and constructing a real aperture radar two-dimensional echo model; Constructing a regularized target function based on the real aperture radar two-dimensional echo model; Performing near-end secondary approximation on the regularized target function by using a two-dimensional rapid iterative threshold contraction algorithm, and taking a regularized target function with the minimum value after optimization; Adopting an alternate iteration strategy to solve the regularized objective function after optimization to obtain a result of the two-dimensional angle super-resolution processing after convergence; Traversing all the distance units to obtain an azimuth pitching two-dimensional super-resolution result.

Description

Self-adaptive rapid two-dimensional angle super-resolution method of real aperture phased array radar Technical Field The document relates to the field of real aperture radar super-resolution imaging, in particular to a self-adaptive rapid two-dimensional angle super-resolution method of a real aperture phased array radar. Background The real aperture radar breaks through the limitation that SAR cannot perform forward-looking imaging, compensates the blind area of SAR imaging, can realize real-time imaging due to quicker imaging processing, and is widely applied to civil and military fields such as material delivery, autonomous navigation of a carrier, precise guidance of a missile and the like. However, for large-area and dense PRF sampling, the number of azimuth sampling points increases rapidly, and it is difficult to realize rapid high-resolution imaging. Meanwhile, the angular resolution of the real aperture radar is limited by the aperture size of the radar antenna, and in practical application, it is difficult to arrange a large aperture antenna to meet the requirement of high resolution imaging. The phased array radar can be used for radar imaging, and is a relatively new way for realizing real aperture radar super-resolution imaging by combining with a compressed sensing theory. In literature "Y.Quan,R.Zhang,Y.Li,R.Xu,S.Zhu and M.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 for forming different and random antenna patterns by using a phased array radar, and then, combining with the compressed sensing theory, a target image can be restored by using few samples, so that the limitation of Rayleigh resolution is broken through. Furthermore, the proposed method can achieve a resolution at least 5.5 times higher than that of the actual aperture imaging, but the method still has limitations on the size and freedom of the phased array, and it is difficult to increase the resolution by more than 10 times. Regularization is one of the important methods to achieve super-resolution imaging. In literature "V.M.Patel,G.R.Easley,D.M.Healy andR.Chellappa,"Compressed synthetic aperture radar",IEEE J.Sel.Topics Signal Process.,vol.4,no.2,pp.244-254,Apr.2010.", a generic model is developed by describing the SAR imaging geometry exactly as an observation matrix, and sparse imaging is accomplished by solving the L1-norm regularization problem. However, a large amount of calculation is needed in the aspects of observation matrix construction and scene iteration recovery, and a certain difficulty is brought to practical application. In literature "J.-w.Zou,M.-b.Zhu,X.-p.Li and W.Dong,"Norm regularization method and its application in radar azimuth super-resolution",2013IEEE International Conference of IEEE Region 10(TENCON 2013),pp.1-4,2013.", azimuth resolution is improved by adding different regularization terms. When the regularization term is L2 norm, this is a well-known Tikhonov regularization method, but it involves inversion of large matrices, which is of high complexity. In summary, although the above-mentioned methods can improve the azimuth resolution of the real aperture radar to some extent. However, the single phased array imaging and regularization methods have limited performance improvement, and the methods with better imaging effects have slower corresponding processing speeds, and cannot realize rapid and effective high-resolution imaging. Therefore, an angular resolution method of a real aperture radar is needed to solve the problem that the conventional regularization method is low in processing speed and cannot realize rapid and effective high-resolution imaging. Disclosure of Invention The specification provides a self-adaptive rapid two-dimensional angle super-resolution method of a real aperture phased array radar, which is used for solving the problem that the traditional regularization method is low in processing speed and cannot realize rapid and effective high-resolution imaging. In a first aspect, the present disclosure provides an adaptive fast two-dimensional angular super-resolution method for a real aperture phased array radar, including: Acquiring echo data by using a digital wave beam formed based on array element position phase compensation, and constructing a real aperture radar two-dimensional echo model; Constructing a regularized target function based on the real aperture radar two-dimensional echo model; Performing near-end secondary approximation on the regularized target function by using a two-dimensional rapid iterative threshold contraction algorithm, and taking a regularized target function with the minimum value after optimization; Adopting an alternate iteration strategy to solve the regularized objective function after optimization to obtain a result of the two-dimensional angle