Improved HRR-ATR using hybridization of HMM and eigen-template-matched filtering

In this paper, a new 1-D hybrid Automatic Target Recognition (ATR) algorithm is developed for High Range Resolution (HRR) profiles. The proposed hybrid algorithm combines EigenTemplate based Matched Filtering (ETMF) and Hidden Markov modeling (HMM) techniques to achieve superior HRR-ATR performance. In the algorithm, each HRR test profile is first scored by ETMF which is then followed by independent HMM scoring. The first ETMF scoring step produces a limited number of “most likely” models that are target and aspect dependent. These reduced number of models are then used for improved HMM scoring in the second step. Finally, the individual scores of ETMF and HMM are combined using Maximal Ratio Combining to render a classification decision. The results demonstrate that the hybridization technique achieves improved recognition performance when compared to the independent performances of either ETMF or HMM.