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 resulte are presented for the ten-target MSTAR data set.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Aerospace and Electronic Systems|
|State||Published - Oct 1 2002|