Abstract: A new approach is developed for multi\|perspective synthetic aperture radar (SAR) target feature extraction and classification based on non\|negative time\|frequency matrix decomposition of high range resolution profiles (HRRPs). The developed approach consists of following stages: SAR images are converted into HRRPs through several filtering operations and the time\|frequency matrix of HRRP is constructed by using the matching\|pursuit time\|frequency analysis technique firstly. Then the time\|frequency matrix of HRRP is decomposed into its spectral and temporal component vectors by applying the non\|negative matrix factorization technique. The time\|frequency moment features and sparsity features are extracted from spectral and temporal component vectors. Finally, hidden markov models (HMM) are utilized to model and classify time\|frequency feature sequences. Experimental results with the Moving and Stationary Target Acquisition and Recognition (MSTAR) data show that the proposed approach not only decreases feature dimensions significantly, but also improves the classification accuracy compared to traditional time\|frequency features.

Key words: SAR, Time-frequency analysis, Non-neGative matrix factorization, Feature extraction, Target recoGnition