Radar radiation source individual identification method based on combination of deep learning model and features

Abstract

A radar radiation source individual identification method based on combination of a deep learning model and features comprises the following steps: 1) collecting intermediate frequency AD signal dataemitted by different radars, and intercepting intra-pulse signal data to generate a radar radiation source individual identification sample set; 2) performing normalization processing on radar radiation source individual identification samples and dividing the radar radiation source individual identification samples into a training sample set, a verification sample set and a test sample set; 3) constructing a radar radiation source individual identification model based on combination of the deep learning model and features; 4) training a radar radiation source individual identification model based on combination of the deep learning model and features; 5) using the test sample set to obtain a radar radiation source individual identification model result and a characteristic determination result; and 6) calculating a final identification result by using the radar radiation source individual identification model result and the feature determination result, and counting the identificationaccuracy. The method is high in universality, does not need manual feature extraction and a large amount of priori knowledge, is low in complexity, and is accurate and stable in 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 deep learning model and feature combination. Background technique [0002] Individual identification of radar emitters is an important research topic in the field of communication countermeasures in recent years. It mainly measures and analyzes the emitter signals intercepted by the receiver, and identifies individuals based on the existing prior information. important link. Individual identification of early radar emitters was mainly achieved by template matching with conventional parameters such as carrier frequency, pulse width, and pulse repetition period. With the improvement of radar technology, new system radars are constantly appearing, and the electromagnetic environment is increasingly dense and complex. The traditional method based on the measurement of external characterist...

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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