CN-121995323-A - Near-field MIMO radar sidelobe suppression method based on phase gradient self-focusing and space self-adaptive time-frequency window function

CN121995323ACN 121995323 ACN121995323 ACN 121995323ACN-121995323-A

Abstract

The embodiment of the application provides a near-field MIMO radar sidelobe suppression method based on a phase gradient self-focusing and space self-adaptive time-frequency window function, which is applied to the technical field of radar signal processing. The method comprises the steps of obtaining MIMO radar echo signals of a target scene, generating wave number domain images based on the MIMO radar echo signals, carrying out phase gradient self-focusing processing based on the wave number domain images to obtain phase correction images, carrying out space self-adaptive division based on the phase correction images to obtain a plurality of sub-block images, carrying out window function matching on each sub-block image, determining a target window function corresponding to each sub-block image, windowing the sub-block images based on the target window function to obtain windowed sub-block images, fusing the windowed sub-block images based on a preset fusion strategy to obtain fusion images, and carrying out secondary phase gradient self-focusing processing on the fusion images to obtain a target imaging result. The technical effect of improving the definition of the imaging result is achieved.

Inventors

XIAO ZHENYU
ZHANG YUANJUN
Yao Xianxun
SUN GUOLIN
YANG FAN
TIAN RUIJIAO

Assignees

北京航空航天大学
北京无线电计量测试研究所

Dates

Publication Date: 20260508
Application Date: 20251202

Claims (12)

1. A near-field MIMO radar sidelobe suppression method based on phase gradient self-focusing and space self-adaptive time-frequency window functions is characterized by comprising the following steps: Acquiring a multi-input multi-output MIMO radar echo signal of a target scene, and generating a wave number domain image based on the MIMO radar echo signal; Performing phase gradient self-focusing processing based on the wave number domain image to obtain a phase correction image, wherein the phase gradient self-focusing is used for eliminating phase deviation in the wave number domain image and improving the definition of an image boundary; performing space self-adaptive division based on the phase correction image to obtain a plurality of sub-block images; performing window function matching on each sub-block image, determining a target window function corresponding to each sub-block image, and windowing the sub-block image based on the target window function to obtain a windowed sub-block image, wherein the window function is used for performing weighted calculation on the sub-block image and improving the definition of the sub-block image boundary; fusing the multiple windowed sub-block images based on a preset fusion strategy to obtain a fused image; And carrying out secondary phase gradient self-focusing treatment on the fusion image to obtain a target imaging result.
2. The method according to claim 1, wherein the performing a phase gradient self-focusing process based on the wave number domain image to obtain a phase corrected image includes: Extracting a first two-dimensional imaging subgraph corresponding to each fixed distance direction in the wave number domain image from the wave number domain image; Performing blocking processing based on the first two-dimensional imaging subgraph to obtain a plurality of overlapped blocks; performing phase gradient calculation based on a plurality of overlapped blocks to obtain a global gradient map corresponding to the first two-dimensional imaging subgraph; Calculating to obtain a phase error based on the global gradient map, and performing compensation calculation based on the phase error to obtain a phase correction map corresponding to the first two-dimensional imaging sub-map; The phase corrected image is generated based on a plurality of fixed-distance-wise corresponding phase corrected figures.
3. The method according to claim 2, wherein the performing a blocking process based on the first two-dimensional imaging sub-graph to obtain a plurality of overlapping blocks includes: dividing the first two-dimensional imaging sub-graph into a plurality of overlapping partitions, wherein a single overlapping region in the overlapping partitions occupies half of the overlapping partitions; And moving the center of each overlapped block to the pixel with the highest signal intensity of the overlapped block to obtain a plurality of shifted overlapped blocks.
4. The method according to claim 3, wherein the calculating the phase gradient based on the plurality of overlapping blocks to obtain the global gradient map corresponding to the first two-dimensional imaging sub-graph includes: based on each shifted overlapped block, obtaining a center row and a center column of the shifted overlapped block, and performing windowing calculation based on the center row and the center column to obtain a windowed overlapped block; Carrying out phase gradient calculation based on each windowed overlapped block to obtain a phase gradient value of each windowed overlapped block; And mapping the phase gradient value of each windowing overlapped block to the position of the windowing overlapped block in the first two-dimensional imaging subgraph through interpolation to obtain a global gradient map corresponding to the first two-dimensional imaging subgraph.
5. The method of claim 1, wherein the spatially adaptively dividing based on the phase corrected image to obtain a plurality of sub-block images comprises: Extracting a second two-dimensional imaging subgraph corresponding to the phase correction image from the phase correction image according to each fixed distance direction of the phase correction image; uniformly dividing the second two-dimensional imaging sub-image into a plurality of basic sub-block images with consistent sizes; Calculating a spectrum width value and an energy value of each basic sub-block image, wherein the spectrum width value represents the signal frequency dispersion degree of the basic sub-block image, the energy value represents the target reflection intensity of the basic sub-block image, and the target reflection intensity refers to a core target of MIMO radar imaging; and carrying out self-adaptive refinement division on each basic sub-block image based on the spectrum width value and the energy value to obtain a plurality of sub-block images.
6. The method according to claim 5, wherein calculating, for each of the basic sub-block images, a spectral width value and an energy value of the basic sub-block image comprises: setting a sliding window with a preset size in each basic sub-block image; performing two-dimensional Fourier transform on each sliding window to obtain a wave number domain signal corresponding to the sliding window; and calculating the spectrum width value and the energy value of each basic sub-block image based on the wave number domain signals.
7. The method of claim 5, wherein adaptively refining the partitioning of each of the basic sub-block images based on the spectral width values and the energy values to obtain a plurality of sub-block images, comprises: calculating an energy threshold value and a spectrum width threshold value of the second two-dimensional imaging sub-graph based on the spectrum width values and the energy values of the plurality of basic sub-block images; for each basic sub-block image, dividing the basic sub-block image into a plurality of primary refinement sub-blocks when the spectral width value is greater than the spectral width threshold or the energy value is greater than the energy threshold; for each primary refinement sub-block, dividing the primary refinement sub-block into a plurality of secondary refinement sub-blocks when the spectral width value of the primary refinement sub-block is greater than the spectral width threshold or the energy value of the primary refinement sub-block is greater than the energy threshold; based on the plurality of basic sub-block images, the plurality of primary refinement sub-blocks, and the plurality of secondary refinement sub-blocks, a plurality of sub-block images are obtained.
8. The method according to any one of claims 1-7, wherein fusing the plurality of windowed sub-block images based on a preset fusion policy to obtain a fused image includes: outwards expanding each windowed sub-block image to obtain expanded sub-block images, wherein an overlapping area exists between the expanded sub-block images; Determining two target window functions corresponding to two expansion sub-block images aiming at each two adjacent expansion sub-block images; Determining a target fusion mode of two adjacent expansion sub-block images based on a preset fusion strategy and function types corresponding to the two target window functions; And fusing each overlapping area based on a fusion mode corresponding to each adjacent expansion sub-block image to obtain the fused image.
9. A near-field MIMO radar sidelobe suppression device based on phase gradient self-focusing and spatial self-adaptive time-frequency window functions, comprising: The acquisition module is used for acquiring MIMO radar echo signals of a target scene and generating wave number domain images based on the MIMO radar echo signals; The first processing module is used for carrying out phase gradient self-focusing processing based on the wave number domain image to obtain a phase correction image, wherein the phase gradient self-focusing is used for eliminating phase deviation in the wave number domain image and improving the definition of an image boundary; the second processing module is used for carrying out space self-adaptive division based on the phase correction image to obtain a plurality of sub-block images; The third processing module is used for carrying out window function matching on each sub-block image, determining a target window function corresponding to each sub-block image, and windowing the sub-block images based on the target window function to obtain windowed sub-block images, wherein the window function is used for carrying out weighted calculation on the sub-block images and improving the definition of the sub-block image boundary; the fourth processing module is used for fusing the plurality of windowed sub-block images based on a preset fusion strategy to obtain a fused image; And a fifth processing module, configured to perform secondary phase gradient self-focusing processing on the fused image, to obtain a target imaging result.
10. An electronic device is characterized by comprising a memory and a processor; The memory stores computer-executable instructions; The processor executing computer-executable instructions stored in the memory, causing the processor to perform the method of any one of claims 1-8.
11. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-8.
12. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-8.

Description

Near-field MIMO radar sidelobe suppression method based on phase gradient self-focusing and space self-adaptive time-frequency window function Technical Field The application relates to the technical field of radar signal processing, in particular to a near-field MIMO radar sidelobe suppression method based on phase gradient self-focusing and space self-adaptive time-frequency window functions. Background The near-field MIMO radar is a novel radar technology combining multiple input and output with near-field radar imaging scenes, has the core value of solving the contradiction between high resolution and compact structure of the traditional radar, and is widely applied to various fields at present. In near-field MIMO radar imaging, side lobes exist as main interference factors of radar imaging, and therefore, in order to improve imaging accuracy and definition, effective suppression processing for the side lobes is required. In the prior art, a main mode aiming at near-field MIMO radar imaging sidelobe suppression is that window functions are utilized to realize sidelobe suppression. Essentially, the echo signals of the near-field MIMO radar are subjected to non-uniform weighting treatment, so that the variability of time domain truncation in the echo signals is reduced, and frequency domain spectrum leakage is further reduced, and side lobe suppression is realized. Because sidelobe suppression is mainly realized through a fixed window function in the prior art, the technical problem of low definition of an imaging result exists in the prior art. Disclosure of Invention The embodiment of the application provides a near-field MIMO radar sidelobe suppression method based on a phase gradient self-focusing and space self-adaptive time-frequency window function, which is used for achieving the technical effect of definition of an imaging result. In a first aspect, an embodiment of the present application provides a near-field MIMO radar sidelobe suppression method based on a phase gradient self-focusing and space self-adaptive time-frequency window function, including: Acquiring MIMO radar echo signals of a target scene, and generating wave number domain images based on the MIMO radar echo signals; the phase gradient self-focusing is used for eliminating phase deviation in the wave number domain image and improving the definition of the image boundary; performing space self-adaptive division based on the phase correction image to obtain a plurality of sub-block images; Window function matching is carried out on each sub-block image, an objective window function corresponding to each sub-block image is determined, and windowing is carried out on the sub-block images based on the objective window function to obtain windowed sub-block images; fusing the multiple windowed sub-block images based on a preset fusion strategy to obtain a fused image; and carrying out secondary phase gradient self-focusing treatment on the fusion image to obtain a target imaging result. Optionally, in one possible implementation, performing phase gradient self-focusing processing based on the wave number domain image to obtain a phase correction image includes: extracting a first two-dimensional imaging subgraph corresponding to each fixed distance direction from the wave number domain image aiming at each fixed distance direction in the wave number domain image; Performing blocking processing based on the first two-dimensional imaging subgraph to obtain a plurality of overlapped blocks; performing phase gradient calculation based on a plurality of overlapped blocks to obtain a global gradient map corresponding to the first two-dimensional imaging subgraph; Calculating to obtain a phase error based on the global gradient map, and performing compensation calculation based on the phase error to obtain a phase correction sub-map corresponding to the first two-dimensional imaging sub-map; a phase correction image is generated based on the plurality of fixed-distance phase correction maps. Optionally, in one possible implementation manner, the partitioning processing is performed based on the first two-dimensional imaging sub-graph, so as to obtain a plurality of overlapped partitions, including: Dividing the first two-dimensional imaging sub-graph into a plurality of overlapping partitions, wherein a single overlapping region in the overlapping partitions occupies half of the overlapping partitions; And moving the center of each overlapped block to the pixel with the highest signal intensity of the overlapped block to obtain a plurality of shifted overlapped blocks. Optionally, in one possible implementation manner, performing phase gradient calculation based on the plurality of overlapping blocks to obtain a global gradient map corresponding to the first two-dimensional imaging sub-graph, including: based on each shifted overlapped block, obtaining a center row and a center column of the shifted overlapped block, and performing windowing c