Radar Working State Recognition Based on the Unsupervised and Incremental Method

Huiqin Li,Hui Zhang,Chuan He,Jianwei Zhan,Yanling Li,Tomasz Wandowski

doi:10.1155/2021/8673046

Abstract

Radar working state recognition is the basis of cognitive electronic countermeasures. Aiming at the problem that the traditional supervised recognition technology is difficult to obtain prior information and process the incremental signal data stream, an unsupervised and incremental recognition method is proposed. This method is based on a backpropagation (BP) neural network to construct a recognition model. Firstly, the particle swarm optimization (PSO) algorithm is used to optimize the preference parameter and damping factor of affinity propagation (AP) clustering. Then, the PSO-AP algorithm is used to cluster unlabeled samples to obtain the best initial clustering results. The clustering results are input as training samples into the BP neural network to train the recognition model, which realizes the unsupervised recognition. Secondly, the incremental AP (IAP) algorithm based on the K -nearest neighbor (KNN) idea is used to divide the incremental samples by calculating the closeness between samples. The incremental samples are added to the BP recognition model as a new known state to complete the model update, which realizes incremental recognition. The simulation experiments on three types of radar data sets show that the recognition accuracy of the proposed model can reach more than 83%, which verifies the feasibility and effectiveness of the method. In addition, compared with the AP algorithm and K -means algorithm, the improved AP method improves 59.4%, 17.6%, and 53.5% in purity, rand index (RI), and F -measure indexes, respectively, and the running time is at least 34.8% shorter than the AP algorithm. The time of processing incremental data is greatly reduced, and the clustering efficiency is improved. Experimental results show that this method can quickly and accurately identify radar working state and play an important role in giving full play to the adaptability and timeliness of the cognitive electronic countermeasures.

Highlights

With the diversity of radar systems, the complexity of electromagnetic signals, the intelligence of the countermeasure targets, and the development application of new antijamming technologies, the combat effectiveness of traditional electronic countermeasure systems has gradually declined
The affinity propagation (AP) clustering purity was selected as the fitness function, and the preference and damping factor were selected as the particle dimensions
This paper proposes an unsupervised incremental radar working state recognition model to quickly and accurately identify the category to which the state belongs and determine whether there is a new category when facing the continuous incoming signal data stream

Summary

Introduction

With the diversity of radar systems, the complexity of electromagnetic signals, the intelligence of the countermeasure targets, and the development application of new antijamming technologies, the combat effectiveness of traditional electronic countermeasure systems has gradually declined. To quickly and accurately complete the prelabeling of initial samples without prior information and the online division of incremental samples, achieve the known and unknown working state recognition of incremental signal samples, and further improve cognitive electronic countermeasure system efficiency, this paper uses label samples generated by improved AP clustering as a training set of the BP model. It generates an unsupervised and incremental radar working state recognition model.

PSO-AP Algorithm

C Incremental sample

Experimental Results and Analysis

A B APAR

Conclusions