Radar emitter individual recognition method based on deep learning model and feature combination

Abstract

A radar emitter individual identification method based on the combination of deep learning model and feature, the steps are: 1) collect the intermediate frequency AD signal data emitted by different radars, and intercept the intrapulse signal data to generate a radar emitter individual identification sample set; Radar emitter individual identification samples are normalized and divided into training sample set, verification sample set and test sample set; 3) Build a radar emitter individual identification model based on deep learning model and feature combination; 4) Training is based on deep learning The radar emitter individual recognition model combined with the model and features; 5) Obtain the radar emitter individual recognition model results and feature judgment results with the test sample set; 6) Combine the radar emitter individual recognition model results and feature judgment results to calculate the final recognition result And statistical recognition accuracy. The invention has strong universality, does not need artificial feature extraction and a large amount of prior knowledge, and has the advantages of low complexity and accurate and stable classification results.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a radar radiation source individual identification method based on a deep learning model and feature combination. Background technique [0002] Radar radiation source individual identification is an important research topic in the field of communication countermeasures in recent years. It mainly measures and analyzes the radiation source signal intercepted by the receiver, and identifies the individual according to the existing prior information. It is the process of electronic reconnaissance. important part. Individual identification of early radar radiation sources was mainly achieved by template matching of conventional parameters such as carrier frequency, pulse width and pulse repetition period. With the improvement of radar technology, new radar systems are emerging, and the electromagnetic environment is increasingly dense and complex. Traditio...

AI Technical Summary

Problems solved by technology

This method solves the problem of insufficient utilization of signal timing features in the traditional radar emitter individual identification, and simplifies the complex data preprocessing of the traditional method, retains more original signal information, and combines the model prediction results with the judgment results based on features Joint calculation to obtain more reliable individual identification results of radar emitters

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