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Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence

Yifan Zhang1, Xunzhang Gao2, Xuan Peng3

  • 1College of Electronic Science, National University of Defense Technology, Changsha 410073, China. zhangyifan16@nudt.edu.cn.

Sensors (Basel, Switzerland)
|May 19, 2018
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Summary
This summary is machine-generated.

A new Attention-based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) model improves High Resolution Range Profile (HRRP) recognition. This method effectively handles high-dimensional and noisy radar data for better target recognition.

Keywords:
HRRPRATRRTRBMattention mechanism

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Area of Science:

  • Radar Automatic Target Recognition (RATR)
  • Machine Learning
  • Signal Processing

Background:

  • Traditional High Resolution Range Profile (HRRP) recognition methods struggle with high-dimensional sequential data and noise.
  • Efficient modeling of temporal and spatial correlations in HRRP data is challenging.

Purpose of the Study:

  • To propose a novel stochastic neural network model, the Attention-based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM), for enhanced HRRP recognition.
  • To address the limitations of traditional methods in handling high-dimensional and noisy radar data.

Main Methods:

  • Utilized Recurrent Temporal Restricted Boltzmann Machine (RTRBM) for discriminative feature extraction from HRRP sequences.
  • Incorporated an attention mechanism to select the most relevant features for recognition.
  • Leveraged the ability of RTRBM to capture temporal and spatial correlations between adjacent HRRPs.

Main Results:

  • The proposed ARTRBM model demonstrated superior performance on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset compared to traditional methods.
  • The model effectively extracts, selects, and utilizes correlation information between adjacent HRRPs.
  • ARTRBM showed robust performance even with noise-corrupted HRRP data.

Conclusions:

  • The ARTRBM model is effective for HRRP recognition, particularly for high-dimensional and noise-corrupted data.
  • The combination of RTRBM and the attention mechanism enhances feature extraction and selection capabilities.
  • The proposed model offers a promising advancement in Radar Automatic Target Recognition.