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.
Highlights
The construction of an optimal classifier based on limited High-resolution range profile (HRRP) signals to correctly extract, learn, and fuse HRRP features, in order to improve the target recognition ability is an open research problem
We propose to learn features contained in the HRRP data, by exploiting the acquisition process of HRRP to extract a HRRP sequence, and generate multi-domain sequence features in time, frequency- and time-frequency domains
A novel algorithm for HRRP target recognition based on bidirectional Long Short-Term Memory (BLSTM) and Hidden Markov Model (HMM) is proposed, which, on the one hand, extracts and fuses the multi-domain HRRP sequence features effectively, using a multi-input approach with primary and secondary branches
Summary
On the one hand, exploit advantages of both LSTM and CNN; and on the other hand, effectively and automatically fuse the features that are irrelevant in a physical sense, thereby greatly improving the recognition performance These algorithms provide ideas for exploring the feature fusion of different data domains in HRRP. In contrast to traditional recognition algorithms, we first extract 2-D sequence features from the formation process of HRRP, and generate different data domains from these 2-D sequence features Based on these sequence features, a novel target identification method is presented by combining bidirectional. The proposed algorithm first learns and fuses the sequence features of different data domains, and combines these with the sequence features of the target multi-aspect, to achieve recognition.
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