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

Clicks: 229
ID: 270313
2019
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Abstract
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.
Reference Key
gao2019electronicsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fei Gao;Teng Huang;Jun Wang;Jinping Sun;Amir Hussain;Huiyu Zhou;Gao, Fei;Huang, Teng;Wang, Jun;Sun, Jinping;Hussain, Amir;Zhou, Huiyu;
Journal Electronics
Year 2019
DOI
10.3390/electronics8050535
URL
https://www.mdpi.com/2079-9292/8/5/535
Keywords
Deep learning human–machine interaction recurrent neural network automatic target recognition

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