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Open AccessArticle

A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles

by 1,†, 1,*,†, 1,*, 1, 2 and 3
1
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, Scotland, UK
3
Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2019, 8(5), 535; https://doi.org/10.3390/electronics8050535
Received: 4 April 2019 / Revised: 4 May 2019 / Accepted: 8 May 2019 / Published: 13 May 2019
(This article belongs to the Special Issue Radar Sensor for Motion Sensing and Automobile)
Radars, as active detection sensors, are known to play an important role in various intelligent devices. Target recognition based on high-resolution range profile (HRRP) is an important approach for radars to monitor interesting targets. Traditional recognition algorithms usually rely on a single feature, which makes it difficult to maintain the recognition performance. In this paper, 2-D sequence features from HRRP are extracted in various data domains such as time-frequency domain, time domain, and frequency domain. A novel target identification method is then proposed, by combining bidirectional Long Short-Term Memory (BLSTM) and a Hidden Markov Model (HMM), to learn these multi-domain sequence features. Specifically, we first extract multi-domain HRRP sequences. Next, a new multi-input BLSTM is proposed to learn these multi-domain HRRP sequences, which are then fed to a standard HMM classifier to learn multi-aspect features. Finally, the trained HMM is used to implement the recognition task. Extensive experiments are carried out on the publicly accessible, benchmark MSTAR database. Our proposed algorithm is shown to achieve an identification accuracy of over 91% with a lower false alarm rate and higher identification confidence, compared to several state-of-the-art techniques. View Full-Text
Keywords: automatic target recognition; human–machine interaction; recurrent neural network; deep learning automatic target recognition; human–machine interaction; recurrent neural network; deep learning
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Gao, F.; Huang, T.; Wang, J.; Sun, J.; Hussain, A.; Zhou, H. A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles. Electronics 2019, 8, 535.

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