Quantization of Multiaspect Scattering Data: Target Classification and Pose Estimation

Yanting Dong, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)3105-3114
Number of pages10
JournalIEEE Transactions on Signal Processing
Issue number12
StatePublished - Dec 1 2003
Externally publishedYes


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