Information-theoretic bounds on target recognition performance from laser radar data

Laser radar systems historically offer rich data sets for automatic target recognition (ATR). ATR algorithm development for laser radar has focused on achieving real-time performance with current hardware. Our work addresses the issue of understanding how much information can be obtain from the data, independent of any particular algorithm. We present Cramer-Rao lower bounds on target pose estimation based on a statistical model for laser radar data. Specifically, we employ a model based on the underlying physics of a coherent-detection laser radar. Most ATR algorithms for laser radar data are designed to be invariant with respect to position and orientation. Our information-theoretic perspective illustrates that even algorithms that do not explicitly involve the estimation of such nuisance parameters are still affected by them.