An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification results are presented for the ten-target MSTAR data set.
Identification of ground targets from sequential high-range-resolution radar signatures
IEEE Transactions on Aerospace and Electronic Systems ; 38 , 4 ; 1230-1242
2002-10-01
676812 byte
Article (Journal)
Electronic Resource
English
Identification of Ground Targets From Sequential High-Range-Resolution Radar Signatures
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