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Synthetic aperture radar (SAR) automatic target recog- nition (ATR) has been receiving more and more attention in the past two decades. But the problem of how to overcome SAR target am- biguities and azimuth angle variations has still left unsolved. In this paper, a multi-scale local phase quantization plus biomimetic pattern recognition (BPR) method is presented to solve these two di-culties. By applying multiple scales local phase quantization (LPQ) on the ob- served SAR images, the blur and azimuth invariant features can be extracted, and these features are fusion at consecutive multiple scales to achieve better results. Then PCA method is applied to further re- duce the feature dimension and achieve its e-ciency. Finally, high dimensional space geometry covering method based on BPR theory is adopted to construct hyper sausage neuron links for target recognition. Experiments on the MSTAR database show that the proposed method can achieve satisfying recognition accuracy compared with other state- of-the-art methods.

A MULTI-SCALE LOCAL PHASE QUANTIZATION PLUS BIOMIMETIC PATTERN RECOGNITION METHOD FOR SAR AUTOMATIC TARGET RECOGNITION