A multi-scale temporal model for multi-view radar high-resolution range profile recognition

In the radar automatic target recognition (RATR) community, high-resolution range profile (HRRP) obtained from the target has been widely utilized for many recognition techniques. The information provided by one single HRRP is limited and unstable, utilizing multiple HRRPs for recognition is a better choice. However, common method such as the hidden Markov model (HMM) is not good at modeling high dimensional data and can only capture local dependencies. In this paper, a multi-scale temporal model is developed from the conditional restricted Boltzmann machine (CRBM). The proposed model can efficiently discover stochastic patterns in high dimensional data just as RBMs generally do. Besides that, it can capture multiple scales of temporal dependencies, which is especially beneficial when the angular sampling rate at testing phase is different from that at training phase. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database validate the efficiency and robustness of the proposed method under various conditions.

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