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.
Techniques for automatic target recognition (ATR) in forward-looking infrared (FLIR) data based on Grenander's pattern theory are revisited. The goal of this work is to unify two techniques: one for multi-target detection and recognition of pose and target type, and another for structured inference of forward-looking infrared (FLIR) thermal states of complex objects. The multi-target detection/recognition task is accomplished through a Metropolis-Hastings jump-diffusion process that iteratively samples a Bayesian posterior distribution representing the desired parameters of interest in the FLIR imagery. The inference of the targets' thermal states is accomplished through an expansion in terms of "eigentanks" derived from a principle component analysis over target surfaces. These two techniques help capture much of the variability inherent in FLIR data. Coupled with future work on rapid detection and penalization strategies to reduce false alarms, we strive for a unified technique for FLIR ATR following the pattern-theoretic philosophy that may be implemented for practical applications.