Abstract
In this paper, a novel stochastic neural network model named Attention based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed for the poor performance of the traditional HRRP recognition methods on high dimensional sequential data and noisy data. RTRBM is efficient to model high dimensional HRRP sequence because it can extract the information of temporal and spatial correlation between adjacent HRRPs. Attention mechanism is used in sequential data recognition tasks, making the model pay more attention to the major features of recognition. Therefore, the combination of RTRBM and attention mechanism makes our mode extract more internal related features effectivly and choose the important parts of the extracted features. Experiment results show that our proposed model outperforms other traditional methods, indicating that ARTRBM extracts, selects and utilizes the correlation information between adjacent HRRPs effectively, and is suitable for high dimensional data or noise corrupted data.
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