Title: Research on the target recognition of marine surveillance radar based on ensemble learning

Authors: Lingang Wu; Shengliang Hu; Jun Zhang; Xueman Fan

Addresses: College of Weaponry Engineering, Naval University of Engineering, 430033 Wuhan, China ' College of Weaponry Engineering, Naval University of Engineering, 430033 Wuhan, China ' College of Weaponry Engineering, Naval University of Engineering, 430033 Wuhan, China ' Navy Submarine Academy, 266000 Tsingtao, China

Abstract: The existence of corner reflector decoy makes the marine surveillance radar to be caught in severe challenges of target recognition. In order to enhance the accuracy of recognition, an ensemble classifier for marine targets is created based on the self-built dataset of high resolution range profile (HRRP). In addition, a confidence evaluation algorithm based on non-parametric estimation of probability density is proposed to reject unknown decoy target outside the database. With taking different interference conditions into consideration respectively, the comparison experiment between ensemble classifier and single classifier is carried out. The results show that the ensemble classifier is significantly better, which has strong robustness to noise as well as rejection ability to unknown target, and the classification accuracy can reach 92.63% under ideal conditions. This paper proves the feasibility of ensemble learning for maritime target recognition, and provides a reliable classification algorithm when the sample information is sufficient.

Keywords: corner reflector; target recognition; ensemble learning; high resolution range profile; HRRP; confidence evaluation.

DOI: 10.1504/IJICT.2022.124327

International Journal of Information and Communication Technology, 2022 Vol.21 No.1, pp.20 - 36

Received: 21 Aug 2020
Accepted: 10 Oct 2020

Published online: 22 Jul 2022 *

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