A novel HRRP target recognition method based on LSTM and HMM decision-making

Tu, Jun and Huang, Teng and Liu, Xusong and Gao, Fei and Yang, Erfu; (2019) A novel HRRP target recognition method based on LSTM and HMM decision-making. In: 2019 25th IEEE International Conference on Automation and Computing. IEEE, GBR. ISBN 9781861376664 (https://doi.org/10.23919/IConAC.2019.8895040)

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Abstract

Feature learning is a key step of target recognition for high-resolution range profile (HRRP). In traditional methods, Hidden Markov Model (HMM) can learn the features of HRRP target aspect information. However, the contextual correlations of HRRPs are ignored due to the independence assumptions in HMM, which brings many limitations to feature learning and weakens the generalization performance. On the contrary, the Long Short-Term Memory (LSTM) network can learn the contextual correlations of HRRPs, but not the target aspect information. To overcome the limitations in these feature learning methods, a new HRRP target recognition method that combines LSTM with HMM decision-making is proposed in this paper. The method consists of two branches: one is target recognition based on HMMs directly; the other is that the latent correlations of HRRPs are extracted by LSTM network and then use HMMs to do target recognition. Finally, the recognition result is obtained by making joint decisions between the two branches. The HRRPs are generated by the inversion of real radar images and the experimental results show that the proposed algorithm outperforms both the HMM and LSTM method.

  • Item type:Book Section
    ID code:72047
    Dates:
    Date
    Event
    11 November 2019
    Published
    21 June 2019
    Accepted
    Notes:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:16 Apr 2020 09:22
    Last modified:08 Apr 2024 14:00
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/72047
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