The classification ground vehicle targets from the MSTAR (Moving and Stationary Target Acquisition and Recognition) database is investigated using Gaussian-mixture hidden Markov models (gHMMs) and synthetic aperture radar images. The gHMMs employ features extracted from High Range Resolution (HRR) radar signal magnitude versus range profiles of the targets. Feature enhancement is made using Cetin's point-based reconstruction technique. The impact on classification accuracy across numbers of hidden states and sequence length is explored using separate training and testing data. Multiple gHMM classifier outputs are fused according to various decision rules across which classification performance is explored.
The classification of three types of ground vehicle targets from the MSTAR (Moving and Stationary Target Acquisition and Recognition) database is investigated using hidden Markov models (HMMs) and synthetic aperture radar images. The HMMs employ training sets of six power spectrum features extracted from High Range Resolution (HRR) radar signal magnitude versus range profiles of the targets for uniform sequences of aspect angles (7 degree separation). Classification accuracy versus numbers of hidden states (from 3 to 30), sequence length (3, 10, 15, and 30), and discretization level of the features (10 and 30 levels) is explored using test and validation data. Best classification (94% correct) is achieved for 3 hidden states, a sequence length of 30, and 10 feature levels.
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