TARGET CLASSIFICATION BASED ON RADAR TRACK USING BILSTM

Hanqing Li, Sheng Luo*, Hongyu Wang, Yanhua Wang, Liang Zhang, Yang Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The track information is an important feature for radar target recognition. In this paper, the authors propose to classify targets based on radar track using bi-directional long short-term memory (BiLSTM) and self-attention mechanism. More specifically, the forward and backward operation in BiLSTM is able to capture temporal dependence of the track. The resulting deep resprentations of the track is refined by a self-attention mechanism to enhance the most discriminative parts. A fully connected layer followed by softmax operator is used to determine the type of the track. Experimental results show that the proposed method outperforms conventional algorithms.

Original languageEnglish
Title of host publicationIET Conference Proceedings
PublisherInstitution of Engineering and Technology
Pages369-373
Number of pages5
Volume2020
Edition9
ISBN (Electronic)9781839535406
DOIs
Publication statusPublished - 2020
Event5th IET International Radar Conference, IET IRC 2020 - Virtual, Online
Duration: 4 Nov 20206 Nov 2020

Conference

Conference5th IET International Radar Conference, IET IRC 2020
CityVirtual, Online
Period4/11/206/11/20

Keywords

  • BILSTM
  • RADAR TRACK
  • SELF-ATTENTION MECHANISM

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Li, H., Luo, S., Wang, H., Wang, Y., Zhang, L., & Li, Y. (2020). TARGET CLASSIFICATION BASED ON RADAR TRACK USING BILSTM. In IET Conference Proceedings (9 ed., Vol. 2020, pp. 369-373). Institution of Engineering and Technology. https://doi.org/10.1049/icp.2021.0793