An integrated method for scanning radar super-resolution imaging detection

Abstract

The invention provides an integrated method for scanning radar super-resolution imaging and detection, which belongs to the technical field of radar imaging. The present invention utilizes the low-rank sparse prior information of the target scene as a constraint to construct an objective function, and iteratively solves the objective function through addition, and at the same time realizes super-resolution imaging and detection of interested sparse targets. Compared with conventional imaging detection and target detection methods, the present invention can realize the integration of sensing target imaging detection and lay a good foundation for further target recognition.

Description

technical field [0001] The invention belongs to the technical field of radar imaging, in particular to an integrated method for scanning radar super-resolution imaging and detection. Background technique [0002] The realization of high-resolution imaging and target detection in the forward-looking area of ​​airborne radar is of great significance for improving the ability of pilots to observe the front area, guiding weapons to accurately strike targets, and implementing airborne security. However, due to the blind area of ​​forward-looking imaging in the conventional single-base synthetic aperture radar and Doppler beam sharpening technology, the imaging detection of the forward-looking area of ​​the platform cannot be realized; the real beam image of the forward-looking area can be obtained by antenna scanning, but due to the size of the antenna However, the azimuth resolution of the real beam image is relatively low, which makes target detection difficult. [0003] The c...

AI Technical Summary

Problems solved by technology

Due to the separation of imaging and detection, on the one hand, the processing efficiency is reduced. On the other hand, the performance of imaging will directly affect the performance of target detection, resulting in the transmission of errors.

In the prior art, for real beam images, a regularized super-resolution imaging method based on the lp norm is proposed, and then based on the imaging results, the target of interest can be detected, which makes the target detection process inevitably affected by the super-resolution performance; while for SAR imaging results, a target detection algorithm proposed, the method first uses the superpixel generation algorithm to segment the image, then adaptively estimates the distribution parameters of each pixel clutter based on the superpixel, and finally uses hierarchical clustering to obtain the target detection result , the method involves many steps, and the detection performance is easily affected by front-end imaging and parameter estimation performance

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