Multispectral sensor fusion for ground-based target orientation estimation: FLIR, LADAR, HRR

In our earlier work, we focused on pose estimation of ground-based targets as viewed via forward-looking passive infrared (FLIR)(1) systems and laser radar (LADAR)(2) imaging sensors.(3) In this paper, we will study individual and joint sensor performance to provide a more complete understanding of our sensor suite. We will also study the addition of a high range-resolution radar (HRR).(4) Data from these three sensors are simulated using CAD models for the targets of interest in conjunction with XPATCH range radar simulation software, Silicon Graphics workstations and the PRISM infrared simulation package. Using a Lie Group representation of the orientation space and a Bayesian estimation framework, we quantitatively examine both pose-dependent variations in performance, and the relative performance of the aforementioned sensors via mean squared error analysis. Using the Hilbert-Schmidt norm as an error metric,(6) the minimum mean squared error (MMSE) estimator is reviewed and mean squared error (MSE) performance analysis is presented. Results of simulations are presented and discussed. In our simulations, FLIR and HRR sensitivities were characterized by their respective signal-to-noise ratios (SNRs) and the LADAR by its carrier-to-noise ratio (CNR). These figures-of-merit can, in turn, be related to the sensor, atmosphere, and target parameters for scenarios of interest.