CN-121995324-A - Near-field MIMO radar data processing method based on multistage background suppression and target enhancement

CN121995324ACN 121995324 ACN121995324 ACN 121995324ACN-121995324-A

Abstract

The embodiment of the application provides a near-field MIMO radar data processing method based on multistage background suppression and target enhancement, which is applied to the technical field of radar signal processing. The method comprises the steps of obtaining a real-time background template and a real-time image frame of a target scene, calculating differential image data corresponding to the real-time image frame based on the real-time background template and the real-time image frame, carrying out Gaussian filtering processing on the differential image data to obtain Gaussian filtered image data, carrying out morphological processing on the Gaussian filtered image data to obtain morphological image data, carrying out target enhancement processing on the morphological image data to obtain target enhanced image data corresponding to the real-time image frame, and outputting the target enhanced image data. The technical effect of improving radar imaging precision 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 (10)

1. A near-field MIMO radar data processing method based on multistage background suppression and target enhancement is characterized by comprising the following steps: Acquiring a real-time background template and a real-time image frame of a target scene; calculating differential image data corresponding to the real-time image frame based on the real-time background template and the real-time image frame; Carrying out Gaussian filtering processing on the differential image data to obtain Gaussian filtered image data; Carrying out morphological processing on the Gaussian filtered image data to obtain morphological image data; Performing target enhancement processing on the morphological image data to obtain target enhancement image data corresponding to the real-time image frame; And outputting the target enhanced image data.
2. The method of claim 1, wherein the acquiring the real-time background template of the target scene comprises: Acquiring a plurality of empty field image frames of the target scene in an empty field state; generating an initial background template based on the plurality of empty field image frames; acquiring a current image frame of the target scene in a preset period; and updating the initial background template according to the current image frame to obtain the real-time background template.
3. The method of claim 2, wherein the generating an initial background template based on the plurality of empty-field image frames comprises: respectively generating an imaging result corresponding to each empty field image frame; And calculating the average value of a plurality of imaging results to obtain the initial background template.
4. The method of claim 2, wherein updating the initial background template based on the current image frame results in the real-time background template, comprising: calculating a normalized difference between the current image frame and the initial background template; when the normalized difference value is lower than a preset threshold value, determining that the current image frame is a blank field image frame which does not contain a target object; and updating the initial background template based on the current image frame to obtain the real-time background template.
5. The method of claim 4, wherein the computing differential image data corresponding to the real-time image frame based on the real-time background template and the real-time image frame comprises: Calculating a preliminary residual error between the real-time background template and the real-time image frame; Calculating the average value of the multi-frame image data of the real-time image frames in the preset sliding window based on the preset sliding window length; Carrying out differential calculation on the average value and the real-time background template to obtain a secondary moving average residual error; And carrying out weighted calculation on the primary residual error and the secondary moving average residual error based on preset weighted parameters to obtain the differential image data corresponding to the real-time image frame.
6. The method of claim 5, wherein performing the target enhancement processing on the morphological image data to obtain target enhanced image data corresponding to the real-time image frame comprises: Sequentially carrying out frequency domain enhancement processing and structure enhancement processing on the morphological image data to obtain structure enhancement image data; Calculating a local mean value of a neighborhood to which each pixel point in the structure enhanced image data belongs; based on each local mean value, calculating to obtain a consistency adjustment factor of each pixel point; And carrying out local consistency adjustment on the structure enhanced image data based on the consistency adjustment factor of each pixel point to obtain the target enhanced image data.
7. The method of claim 6, wherein after performing the target enhancement processing on the morphological image data to obtain target enhanced image data corresponding to the real-time image frame, the method further comprises: normalizing the target enhanced image data to obtain a soft mask; Updating the real-time background template based on the soft mask to obtain an updated real-time background template; Carrying out nonlinear differential fusion weighting calculation based on the preliminary residual error, the secondary moving average residual error and the consistency adjustment factor to obtain nonlinear differential fusion image data; Carrying out Gaussian filtering processing and morphological processing on the nonlinear differential fusion image data respectively to obtain updated morphological image data; the performing target enhancement processing on the morphological image data to obtain target enhanced image data corresponding to the real-time image frame includes: And carrying out target enhancement processing on the updated morphological image data to obtain updated target enhanced image data.
8. A near-field MIMO radar data processing apparatus based on multistage background suppression and target enhancement, comprising: The acquisition module is used for acquiring a real-time background template and a real-time image frame of the target scene; The first processing module is used for calculating differential image data corresponding to the real-time image frame based on the real-time background template and the real-time image frame; the second processing module is used for carrying out Gaussian filtering processing on the differential image data to obtain Gaussian filtered image data; the third processing module is used for carrying out morphological processing on the Gaussian filtered image data to obtain morphological image data; the fourth processing module is used for carrying out target enhancement processing on the morphological image data to obtain target enhancement image data corresponding to the real-time image frame; And a fifth processing module for outputting the target enhanced image data.
9. 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 1-7.
10. 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 1-7.

Description

Near-field MIMO radar data processing method based on multistage background suppression and target enhancement Technical Field The application relates to the technical field of radar signal processing, in particular to a near-field MIMO radar data processing method based on multistage background suppression and target enhancement. Background In scenes such as security inspection, security protection, industrial detection and the like, which have strict requirements on high-precision target identification and background suppression, a radar needs to quickly identify a small target in a complex dynamic environment and simultaneously suppress static and dynamic clutter. In the prior art, the near-field radar imaging method mainly comprises the steps of carrying out differential calculation or self-adaptive filtering processing based on a background template to obtain a radar imaging result aiming at a target scene. Because the background template in the prior art cannot adapt to complex and changeable scenes, the radar signals or the image signals extracted by filtering processing lack of fineness, and therefore the technical problem of low radar imaging precision in the prior art is caused. Disclosure of Invention The embodiment of the application provides a near-field MIMO radar data processing method based on multistage background suppression and target enhancement, which is used for achieving the technical effect of improving radar imaging precision. In a first aspect, an embodiment of the present application provides a near-field MIMO radar data processing method based on multistage background suppression and target enhancement, including: Acquiring a real-time background template and a real-time image frame of a target scene; calculating differential image data corresponding to the real-time image frames based on the real-time background template and the real-time image frames; carrying out Gaussian filtering processing on the differential image data to obtain Gaussian filtered image data; carrying out morphological processing on Gaussian filtered image data to obtain morphological image data; performing target enhancement processing on the morphological image data to obtain target enhancement image data corresponding to the real-time image frame; The target enhanced image data is output. In one possible implementation, acquiring a real-time background template of a target scene includes: Acquiring a plurality of empty field image frames of a target scene in an empty field state; generating an initial background template based on the plurality of empty field image frames; acquiring a current image frame of a target scene in a preset period; And updating the initial background template according to the current image frame to obtain the real-time background template. In one possible implementation, generating an initial background template based on a plurality of empty field image frames includes: Respectively generating an imaging result corresponding to each empty field image frame; And calculating the average value of the imaging results to obtain an initial background template. In one possible implementation, updating the initial background template according to the current image frame to obtain the real-time background template includes: Calculating a normalized difference between the current image frame and the initial background template; When the normalized difference value is lower than a preset threshold value, determining that the current image frame is a blank field image frame which does not contain the target object; and updating the initial background template based on the current image frame to obtain the real-time background template. In one possible implementation, based on the real-time background template and the real-time image frame, differential image data corresponding to the real-time image frame is calculated, including: Calculating a preliminary residual error between the real-time background template and the real-time image frame; Calculating an average value of the real-time image frames among the multi-frame image data in the preset sliding window based on the preset sliding window length; carrying out differential calculation on the average value and the real-time background template to obtain a secondary moving average residual error; And carrying out weighted calculation on the primary residual error and the secondary moving average residual error based on preset weighted parameters to obtain differential image data corresponding to the real-time image frame. In one possible embodiment, performing target enhancement processing on morphological image data to obtain target enhanced image data corresponding to a real-time image frame includes: Sequentially carrying out frequency domain enhancement processing and structure enhancement processing on morphological image data to obtain structure enhancement image data; Calculating a local mean value of a neighborhood to which each