Quantization of Multiaspect Scattering Data: Target Classification and Pose Estimation

Published

Journal Article

In many sensing scenarios, the observed scattered waveforms must be quantized for subsequent transmission over a communication channel. Rate-distortion theory plays an important role in defining the bit rate required to achieve a desired distortion. The distortion is typically defined in the context of signal reconstruction, with the goal of achieving high-fidelity synthesis of the compressed data. For sensing applications, however, the objective is often not simply signal reconstruction but classification performance as well. Other related metrics include target-pose estimation. In this paper, we consider multiaspect wave scattering, in which classification and pose estimation are performed based on the quantized scattering data. Moreover, rate-distortion theory is employed to place bounds on pose-estimation performance when both the target identity and pose are unknown a priori. It is demonstrated that block-coding with Bayes-VQ may yield performance approaching the bound. Example results are presented for measured acoustic waveforms scattered from underwater elastic targets.

Full Text

Duke Authors

Cited Authors

  • Dong, Y; Carin, L

Published Date

  • December 1, 2003

Published In

Volume / Issue

  • 51 / 12

Start / End Page

  • 3105 - 3114

International Standard Serial Number (ISSN)

  • 1053-587X

Digital Object Identifier (DOI)

  • 10.1109/TSP.2003.818998

Citation Source

  • Scopus