| Paper: | SAM-P2.2 | ||
| Session: | Detection and Estimation | ||
| Time: | Tuesday, May 18, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Sensor Array and Multichannel Signal Processing: Physics-based sensor array processing | ||
| Title: | ADAPTIVE MULTI-ASPECT TARGET CLASSIFICATION AND DETECTION WITH HIDDEN MARKOV MODELS | ||
| Authors: | Shihao Ji; Duke University | ||
| Xuejun Liao; Duke University | |||
| Lawrence Carin; Duke University | |||
| Abstract: | We consider target classification and detection based on backscattered observations measured from a sequence of target-sensor orientations. The multi-aspect scattered waves from a given target are modeled with a hidden Markov model (HMM). The targets are assumed concealed and the absolute target-sensor orientation is assumed unknown; therefore, it is only possible to control the angular deplacements between consecutive measurements. The performance of the HMM classifiers/detectors is influeneced by the choice of the angular displacements, the optimization of which motivates adaptive search strategies developed in this paper, based on entropy-driven optimality criteria. The search proceeds in a sequential fashion. Based on the previous observations and the associated angular displacements, one determines the optimal next displacement to perform an associated observation. The search strategies are detailed and example results presented on adaptive classification and detection of underwater targets. | ||
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