Double-path target detection method for complex ground reflection environment

By employing a dual-path target detection method, utilizing an adaptive discrete event model and a dual-path parallel processing architecture, the problem of unstable weak target detection in complex environments by radar is solved. This achieves high detection rate for weak targets and low false alarm rate against strong clutter, thereby improving the robustness and interpretability of radar detection.

CN121069341APending Publication Date: 2025-12-05CIVIL AVIATION FLIGHT UNIV OF CHINA
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Patent Information

Application Number
CN202511247754.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-03
Publication Date
2025-12-05

AI Technical Summary

Technical Problem

Existing radar target detection technologies struggle to simultaneously achieve high detection rates for weak targets and low false alarm rates for strong clutter in complex multipath reflection and dynamic clutter environments. Traditional CFAR algorithms exhibit unstable performance in complex low-altitude near-ground environments and lack adaptability and interpretability to the environment.

Method used

A dual-path target detection method for complex ground reflection environments is adopted. Quantile truncation and discretization are performed through an adaptive discrete event model. A dual-path parallel processing architecture is used to suppress strong clutter and enhance weak targets respectively. Finally, weighted fusion and fixed threshold decision are performed.

Benefits of technology

It improves the robustness and sensitivity of radar target detection, enabling robust detection of weak targets in complex backgrounds, maintaining a low false alarm rate, and possessing good interpretability and scalability.

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Abstract

The invention relates to the technical field of radar signal processing, and discloses a complex ground reflection environment-oriented dual-path target detection method, which comprises the following steps of: performing quantile truncation and discretization processing on original range profile data, mapping a processed signal to a D-channel discrete feature space and generating a D-dimensional discrete feature matrix; the D-dimensional discrete feature matrix is input into a dual-channel parallel processing architecture, a CFAR adaptive detection module is adopted in a first path to suppress strong clutters to generate a first response matrix, and a false attention mechanism is adopted in a second path to enhance weak targets to generate a second response matrix; performing spatial alignment and weighting processing on the first response matrix and the second response matrix, and performing cross-domain confidence fusion to output a comprehensive response matrix; and carrying out fixed threshold binarization processing on the comprehensive response matrix based on a global threshold to obtain a target detection result. The method has both local gain compensation and global adaptive suppression, and can realize more robust detection of a weak target under a complex background.
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