Robust detection method and device for radar extended target under weighted generalized inverse gaussian clutter

By estimating clutter parameters and the inverse expectation of texture components using a weighted generalized inverse Gaussian clutter model, the robustness of extended target detection in radar systems under complex Gaussian clutter environments is solved, enabling real-time processing and efficient detection.

CN120871059BActive Publication Date: 2026-06-19AIR FORCE EARLY WARNING ACADEMY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
AIR FORCE EARLY WARNING ACADEMY
Filing Date
2025-06-27
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

In complex Gaussian clutter environments, the extended target detection robustness of existing radar systems is poor and cannot meet the requirements of real-time processing. Furthermore, traditional detectors require frequent recalculation of the detection threshold, resulting in poor practicality.

Method used

A weighted generalized inverse Gaussian clutter model is adopted. By estimating the clutter parameter vector and the inverse expectation of the texture component, a test statistic is constructed to achieve joint optimization of target energy accumulation and clutter suppression, thereby improving detection robustness.

Benefits of technology

This technology enhances the robustness of radar-extended target detection, meets real-time processing requirements, simplifies the detection process, and improves the efficiency and usability of the detector.

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Abstract

This invention relates to the field of radar target detection technology, and provides a robust method and apparatus for radar extended target detection under weighted generalized inverse Gaussian clutter. The invention uses a clutter prior information vector and a moment estimation order vector to estimate the clutter parameter vector; based on the training sample matrix, the data matrix to be detected, and the estimated values ​​of the clutter parameter vector, it estimates the inverse expectation of the texture component; based on the training sample matrix, the data matrix to be detected, the signal steering vector, and the estimated values ​​of the inverse expectation of the texture component, it constructs a test statistic; and based on the test statistic, it determines the target state. This invention solves the problems of radar extended target detection failing to meet real-time processing requirements and exhibiting poor robustness.
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Citation Information

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