Patch-Wise Autoencoder Based on Transformer for Radar High-Resolution Range Profile Target Recognition

Xianwen Zhang

doi:10.1109/JSEN.2023.3327552

Patch-Wise Autoencoder Based on Transformer for Radar High-Resolution Range Profile Target Recognition

2023-12-01

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AI

DOI：10.1109/JSEN.2023.3327552

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High-resolution range profile (HRRP) is indispensable for modern radar automatic target recognition (RATR) systems and commonly has noisy background and misalignment along the range dimension. In this article, we propose a novel algorithm for HRPP recognition with the improvement of recognition accuracy, stability against range translation, and robustness to noise background. This method divides the HRRP into patches and encodes the patches to latent feature vectors via a multihead attention mechanism, modeling the spatial dependence, eliminating the influence of range translation, and focusing on the target area. A decoder is adopted to reconstruct the HRRP and improve the robustness against the noise. Experiments on measured data prove that the proposed algorithm outperforms other existing methods with recognition accuracy increased significantly, sensitivity to the range translation eliminated, and noise robustness improved. This study provides a promising and effective approach for HRRP target recognition.

Keywords:

Autoencoder

high-resolution range profile (HRRP)

multihead self-attention (MSA)

radar automatic target recognition (RART)

transformer

Patch-Wise Autoencoder Based on Transformer for Radar High-Resolution Range Profile Target Recognition

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