Radar abnormal data intelligent detection and identification method and system

By acquiring multi-channel echo data from a phased array radar system, extracting multi-dimensional features, and constructing a deep learning network, the problems of high accuracy and false alarm rate in radar anomaly detection were solved, enabling the identification of different anomaly types and system adjustments.

CN122410461APending Publication Date: 2026-07-17ZHEJIANG LANJIAN DEFENSE TECH CO LTD
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Patent Information

Application Number
CN202610722523.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-25
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing radar anomaly detection technologies rely on a single parameter, are susceptible to interference from the external environment, and cannot identify the specific type of anomaly, resulting in inaccurate identification and a high false alarm rate.

Method used

By acquiring multi-channel raw echo data from the phased array radar system in real time, extracting multi-dimensional feature vectors in the time, frequency, and spatial domains, constructing an anomaly detection model, and using a multi-branch deep learning network for anomaly detection and identification, a system adjustment command is generated.

Benefits of technology

It improves the accuracy and reliability of anomaly detection in radar systems, enabling the identification of different types of anomalies and the generation of targeted adjustment commands to reduce false alarm rates.

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Abstract

The invention discloses a radar abnormal data intelligent detection and identification method and system, and relates to the technical field of radio, and the method comprises the steps: obtaining multi-channel original echo data from a phased array radar system in real time; multi-dimensional feature vectors are extracted from the original echo data, the multi-dimensional feature vectors including time domain features, frequency domain features and spatial domain features are extracted, and the spatial domain features include directional diagram features after beam forming; constructing an anomaly detection model, inputting the multi-dimensional feature vector into the anomaly detection model, and outputting an anomaly detection result; when the detection result is abnormal, abnormal type identification is carried out; and according to the abnormal type identification result, generating a corresponding adjustment instruction, uploading the abnormal information, and then issuing and executing a system adjustment instruction. According to the invention, the technical problems of inaccurate anomaly identification and high anomaly detection false alarm rate caused by dependence on a single parameter, susceptibility to external environment interference and incapability of identifying the specific type of the anomaly in the prior art are solved.
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