CN-117761693-B - ISAR imaging method based on sparse low-rank decomposition and frequency coefficient redistribution

CN117761693BCN 117761693 BCN117761693 BCN 117761693BCN-117761693-B

Abstract

The invention discloses an ISAR imaging method based on sparse low-rank decomposition and frequency coefficient redistribution, which comprises the steps of obtaining an initial image; and re-distributing a target frequency fraction coefficient based on the RID imaging frame by taking the target region as priori knowledge to obtain a final image. By using the method, the technical problem of low ISAR imaging quality caused by clutter in the existing ISAR imaging method can be solved, and the target resolution is improved while the clutter is suppressed. The invention can be widely applied to the field of image processing.

Inventors

XIE ZHIFENG
CUI LEI
WANG XIAOQING
YANG PEIQING
SU XINGYI

Assignees

中山大学

Dates

Publication Date: 20260505
Application Date: 20231225

Claims (5)

1. An ISAR imaging method based on sparse low-rank decomposition and frequency coefficient redistribution is characterized by comprising the following steps: Acquiring an initial image; Constructing a target area by a sparse low-rank decomposition method based on the initial image; Based on an RID imaging frame, re-distributing a target frequency fraction coefficient by taking the target area as priori knowledge to obtain a final image; the step of reassigning the target frequency fraction coefficient based on the RID imaging frame by taking the target area as priori knowledge to obtain a final image specifically comprises the following steps: based on an RID imaging frame, constructing a frequency coefficient rearrangement operator by taking the target area as priori knowledge; Based on the frequency coefficient rearrangement operator, obtaining a final image by iteratively reassigning a target frequency fraction coefficient; The frequency coefficient rearrangement operator is expressed as follows: Wherein, the Represent the first The target area of the individual distance cell(s), Represent the first The clutter regions of the individual range bins, Representing the frequency coefficient reordering operator, Representing the original reassignment operator, t representing the point in time, ω representing the preset frequency variable, G representing the result of STFT, A first order derivative representing a window function g; The process of reassigning the target frequency fraction coefficient based on the frequency coefficient rearrangement operator is performed iteratively, and the formula is as follows: where N represents the number of iterations and η represents the transformed frequency variable.
2. The ISAR imaging based on sparse low-rank decomposition and frequency coefficient reassignment according to claim 1, wherein the step of constructing a target region by a sparse low-rank decomposition method based on the initial image specifically comprises: Expressing the separation problem of the defocus component and the target strong scattering point region in the initial image as low-rank sparse The problem of norm minimization; for the low rank sparsity And solving the norm minimization problem, and separating the target region by utilizing the low rank of the defocusing component and the sparsity of the target to obtain a target region and a clutter region.
3. The ISAR imaging method based on sparse low-rank decomposition and frequency coefficient reassignment of claim 2, the low-rank sparse The norm minimization problem is formulated as follows: wherein X represents an initial image, L represents a low rank portion, S represents a sparse portion, Representing weights between the low rank portion and the sparse portion, Representation of Norms.
4. An ISAR imaging system, in accordance with claim 1, in which the method for performing sparse low rank decomposition and frequency coefficient reassignment based on comprises: The image acquisition module is used for acquiring an initial image; The sparse low-rank decomposition module is used for constructing a target area through a sparse low-rank decomposition method based on the initial image; And the frequency coefficient redistribution module is used for redistributing the target frequency fraction coefficient based on the RID imaging frame by taking the target area as priori knowledge to obtain a final image.
5. An ISAR imaging apparatus based on sparse low rank decomposition and frequency coefficient reassignment, comprising: At least one processor; At least one memory for storing at least one program; The at least one program, when executed by the at least one processor, causes the at least one processor to implement an ISAR imaging method based on sparse low rank decomposition and frequency coefficient reassignment as claimed in any one of claims 1 to 3.

Description

ISAR imaging method based on sparse low-rank decomposition and frequency coefficient redistribution Technical Field The invention belongs to the field of image processing, and particularly relates to an ISAR imaging method based on sparse low-rank decomposition and frequency coefficient redistribution. Background Synthetic Aperture Radar (SAR) can acquire high resolution images under all-weather conditions for earth observation. However, it is difficult to obtain high quality SAR images of non-cooperative targets due to target motion and the presence of noise or clutter background. Inverse SAR (ISAR) provides another way of signal processing to obtain high resolution images of non-cooperative targets. SAR obtains a synthetic aperture through the motion of a radar platform while assuming the imaging target is stationary. And ISAR uses target motion to image azimuth during synthetic aperture formation. In order to obtain high quality moving images of a mobile target, we need to cut around the target to obtain a target image slice, and then obtain ISAR data by inverting SAR imaging algorithm. Next, the maneuvering target in the SAR image is refocused using an ISAR signal processing method. However, clutter present in defocused SAR target images can significantly reduce the quality of ISAR imaging. Disclosure of Invention In view of this, in order to solve the technical problem of low ISAR imaging quality caused by clutter in the existing ISAR imaging method, in a first aspect, the present invention provides an ISAR imaging method based on sparse low-rank decomposition and frequency coefficient redistribution, the method includes the following steps: Acquiring an initial image; Constructing a target area by a sparse low-rank decomposition method based on the initial image; and based on the RID imaging frame, the target region is taken as priori knowledge, and the target frequency fraction coefficient is redistributed to obtain a final image. The initial image is generated by an RD imaging algorithm. Optionally, the step of constructing the target area by a sparse low-rank decomposition method based on the initial image specifically includes: Expressing the separation problem of the defocus component and the target strong scattering point region in the initial image as a low-rank sparse l 0 norm minimization problem; And solving the low-rank sparse l 0 norm minimization problem, and separating a target region by utilizing the low rank property of the defocusing component and the sparsity of the target to obtain a target region and a clutter region. By this preferred step, the target region is separated from the clutter region, facilitating subsequent reassignment of only the time-frequency representation components belonging to the target. Optionally, the low-rank sparse l 0 norm minimization problem is expressed as follows: s.t.X=L+S Wherein X represents an initial image, L represents a low rank portion, S represents a sparse portion, λ represents the weight between the low rank part and the sparse part, |·| 0 represents the L 0 norm. Optionally, the step of reassigning the target frequency fraction coefficient based on the RID imaging frame with the target area as a priori knowledge to obtain a final image specifically includes: based on an RID imaging frame, constructing a frequency coefficient rearrangement operator by taking the target area as priori knowledge; And based on the frequency coefficient rearrangement operator, obtaining a final image by iteratively reallocating the target frequency division coefficient. Through this preferred step, a frequency coefficient re-allocation operator is constructed to perform frequency coefficient re-allocation on the target region. In some embodiments, the frequency coefficient reordering operator is represented as follows: Wherein R τ,tar get represents the target region of the τ -th distance unit, R τ,background represents the clutter region of the τ -th distance unit, ω 1 (t, ω) represents the frequency coefficient rearrangement operator, ω 0 (t, ω) represents the original reassignment operator, t represents the time point, ω represents the preset frequency variable, G represents the result of STFT, and G' represents the first-order derivative of the window function G. This step is shown as: If ω 0 (t, ω) belongs to the target area of the distance cell If ω 0 (t, ω) belongs to the clutter region of the distance cell, it will not be reassigned, i.e., ω 1 (t, ω) =ω. Optionally, the process of iteratively reassigning the target frequency fraction coefficient is formulated as follows: where N represents the number of iterations and η represents the transformed frequency variable. In this step, the frequency coefficient rearrangement operator is compounded N times, and the expression is rewritten. In a second aspect, the present invention further provides an ISAR imaging system based on sparse low-rank decomposition and frequency coefficient reassignment, the system c