Quantization of multiaspect scattering data: target classification and pose estimation

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 highfidelity 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 targetpose estimation. 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.