Multispectral active-passive sensor fusion for ground-based target orientation estimation

Our work focuses on pose estimation of ground-based targets viewed via multiple sensors including forward-looking infrared radar (FLIR) systems and laser radar (LADAR) range imagers. Data from these two sensors are simulated using CAD models for the targets of interest in conjunction with Silicon Graphics workstations, the PRISM infrared simulation package, and the statistical model for LADAR described by Green and Shapiro. Using a Bayesian estimation framework, we quantitatively examine both pose-dependent variations in performance, and the relative performance of the aforementioned sensors when their data is used separately or optimally fused together. Using the Hilbert-Schmidt norm as an error metric, the minimum mean squared error (MMSE) estimator is reviewed and its mean squared error (MSE) performance analysis is presented. Results of simulations are presented and discussed.