Title :
Hidden Markov models for multiaspect target classification
Author :
Couchman, L. ; Carin, Lawrence
fDate :
7/1/1999 12:00:00 AM
Abstract :
This article presents a new approach for target identification, in which we fuse scattering data from multiple target-sensor orientations. The multiaspect data is processed via hidden Markov model (HMM) classifiers, buttressed by physics-based feature extraction. This approach explicitly accounts for the fact that the target-sensor orientation is generally unknown or “hidden”. Discrimination results are presented for measured scattering data
Keywords :
acoustic wave scattering; feature extraction; hidden Markov models; sensor fusion; signal classification; signal detection; underwater sound; EM scattering; HMM classifiers; discrimination results; hidden Markov models; measured scattering data; multiaspect target classification; multiple target-sensor orientations; physics-based feature extraction; scattering data fusion; target detection; target identification; underwater acoustic scattering; Acoustic measurements; Acoustic scattering; Dictionaries; Electromagnetic scattering; Feature extraction; Fuses; Hidden Markov models; Matching pursuit algorithms; Physics; Target recognition;
Journal_Title :
Signal Processing, IEEE Transactions on
