Elements of information theory
Elements of information theory
Aided and Automatic Target Recognition Based Upon Sensory Inputs From Image Forming Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data
Signal Processing - Special section on information theoretic aspects of digital watermarking
Combining Image Compression and Classification Using Vector Quantization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applications of information theory to pattern recognition and the design of decision trees and trellises
Markov modeling of transient scattering and its application in multi-aspect target classification
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Hidden Markov models for multiaspect target classification
IEEE Transactions on Signal Processing
Efficient mixed-spectrum estimation with applications to targetfeature extraction
IEEE Transactions on Signal Processing
Matching pursuits with a wave-based dictionary
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Hidden markov model networks for multiaspect discriminative features extraction from radar targets
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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We consider the problem of estimating the pose of a target based on a sequence of scattered waveforms measured at multiple target-sensor orientations. Using a hidden Markov model (HMM) representation of the scattered-waveform sequence, pose estimation reduces to estimating the underlying HMM states from a sequence of observations. It is assumed that each scattered waveform must be quantized via an encoding procedure. A distortion D is defined as the error in estimating the underlying HMM states, and the rate R represents the size of the discrete-HMM codebook. Rate-distortion theory is applied to define the minimum rate required to achieve a desired distortion, denoted as R(D). After deriving the rate-distortion function R(D), we demonstrate that discrete-HMM performance based on Lloyd encoding is far from this bound. Performance is improved via block coding, based on Bayes VQ. Results are presented for a canonical HMM problem, and then for multiaspect acoustic scattering from underwater elastic targets. Although the examples presented here are for multiaspect scattering and pose estimation, the results are of general applicability to discrete-HMM state estimation.