Radar Emitter Recognition Based on Parameter Set Clustering and Classification

Abstract

An important task in the Electronic Support Measures (ESM) field is analyzing and recognizing radar signals. Feature extraction is one of the primary key elements of radar emitter recognition algorithms. Current research mainly finds statistical features such as the mean and variance of parameters from pluses as the input features of the classifier. However, data noise in intercepted pulse signals greatly interferes with the accuracy of the extracted statistical features and seriously affects the recognition rate of radar emitters. In this paper, we proposed a method of radar emitter recognition. We first clustered parameter sets to establish a set of frequent items and their corresponding clustering centers. Next, we concatenated the clustering centers of each frequent item into a feature vector associated with the data volume dimensions. Then, we built a decision tree classification model based on the feature vector, and finally we used the learned model for the recognition of unknown radar pulse trains. The simulation results show that the proposed method has better robustness when applied to a variety of data volumes and data noise scenarios compared with long short-term memory (LSTM) and support vector machine (SVM) methods.

High-Quality Radar Pulse Signal Acquisition and Deinterleaving under a Low Signal-to-Noise Ratio with Multi-Layer Particle Swarm Optimization

“…In order to achieve the alignment of PCS, this time offset ∆t j should be estimated according to the CCF of s i (t) and s j (t), which can be calculated by Equation (8).…”

“…Radar electronic warfare (EW) [1,2] based on electromagnetic spectrum competition and confrontation will be an essential part of the future information battlefield. In radar EW, the electronic support measure (ESM) [3][4][5][6][7][8][9] can provide practical military intelligence and battlefield spectrum awareness through the feature estimation of reconnaissance signals. Based on the information obtained by ESM, effective electronic countermeasures (ECM) [10][11][12] can be easily generated to counter specific radar sources.…”

Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework

“…For the improvement of multi-objective tasks, the authors of [ 25 , 26 , 27 ] used the method of adding windows in the time domain, to process data, designed the time processing module and the threshold function of the selection window, and thus realized multi-objective classification. The authors of [ 28 , 29 ] proposed a comprehensive recognition method, based on a traditional classifier and deep learning network. It confirmed the feasibility of identifying unknown signals from known signals, which employed a classifier to train the deep learning network and deduce the center vector of the known data.…”

A Novel Batch Streaming Pipeline for Radar Emitter Classification

“…In electronic warfare, classification of the radar emitters involves several steps [8]. First, pulse descriptor words (PDWs) are generated from the received radar signals.…”