A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

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.

Radar Intra–Pulse Signal Modulation Classification with Contrastive Learning

“…There are mainly two categories of approaches for radar intra-pulse signal modulation classification: the traditional feature extraction-based approaches and the recent deep learning-based ones. For the first category, the algorithms usually extract some useful features from the signal before classification [4][5][6][7][8][9]. The feature extraction methods employed in these algorithms include time-frequency transform using short-time Fourier transform (STFT) in [4], Choi-Williams' time-frequency distribution in [9], power feature extraction using Rihaczek distribution (RD) and Hough transform (HT) in [5], integrated quadratic phase function (IQPF) and fractional Fourier transform (FrFT) in [6], time-frequency transform using Wigner Ville distribution (WVD) and FrFT in [7], and optimal classification atom and improved double-chains quantum genetic algorithm (IDCQGA) in [8].…”