Abstract

Radar electronic reconnaissance is an important part of modern and future electronic warfare systems and is the primary method to obtain non-cooperative intelligence information. As the task requirement of radar electronic reconnaissance, it is necessary to identify the non-cooperative signals from the mixed signals. However, with the complexity of battlefield electromagnetic environment, the performance of traditional recognition system is seriously affected. In this paper, a new recognition method based on optimal classification atom and improved double chains quantum genetic algorithm (IDCQGA) is researched, optimal classification atom is a new feature for radar signal recognition, IDCQGA with symmetric coding performance can be applied to the global optimization algorithm. The main contributions of this paper are as follows: Firstly, in order to measure the difference of multi-class signals, signal separation degree based on distance criterion is proposed and established according to the inter-class separability and intra-class aggregation of the signals. Then, an IDCQGA is proposed to select the best atom for classification under the constraint of distance criterion, and the inner product of the signal and the best atom for classification is taken as the eigenvector. Finally, the extreme learning machine (ELM) is introduced as classifier to complete the recognition of signals. Simulation results show that the proposed method can improve the recognition rate of multi-class signals and has better processing ability for overlapping eigenvector parameters.

Highlights

  • Signal recognition is one of the main research directions in the field of electronic reconnaissance [1,2]

  • In order to solve these three problems, this paper proposes an optimal classification atom feature extraction and recognition method based on distance criterion

  • The simulation shows that the high-density coding method in this paper compresses the search space of the results show that both genetic algorithm (GA) and quantum genetic algorithm (QGA) are trapped in the local optimal value, only the optimal algorithm and improves the convergence speed of the algorithm

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Summary

Introduction

Signal recognition is one of the main research directions in the field of electronic reconnaissance [1,2]. Fast and accurate identification of enemy signals can grasp advantages in battlefield environment perception, information control, and operational command, which has an important impact on the trend of war. Radar signals have the characteristics of changeable intra-pulse modulation. It is a reliable way to improve the recognition ability of radar signals to study the extraction of intra-pulse features. The intra-pulse modulation of radar signals can be divided into intentional modulation and unintentional modulation [3]. Intentional modulation is an artificial modulation mode to improve the detection performance and resist enemy reconnaissance

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