Target identification from multi-aspect high range-resolution radar signatures using a hidden Markov model
Identification of targets using sequential high range-resolution (HRR) radar signatures is studied. Classifiers are designed by using hidden Markov models (HMMs) to characterize the sequential information, in multi-aspect HRR signatures. The higher-order moments together with the target dimension and the number of dominant wavefronts are used as features of the transient HRR waveforms. Classification results are presented for the ten-target MSTAR data set. The example results show that good classification performance and robustness are obtained, although the target features used here are very simple and compact compared with the complex HRR signatures.