In our earlier work, we focused on pose estimation of ground- based targets as viewed via forward-looking passive infrared (FLIR) systems and laser radar (LADAR) imaging sensors. 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). 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, 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.
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 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.
Statistical communication theory is used to develop the structure and performance of quasi-optimal recognition processors for 3D coherent laser radar range imagery. Generalized likelihood-ratio tests and receiver operating characteristics are presented for detection and recognition scenarios involving a variety of unknown object and background parameters.
Statistical detection theory is used to develop the structure and performance of quasioptimal detection processors for 3-D coherent laser radar range imagery. Generalized likelihood-ratio tests (GLRTs) and receiver operating characteristics (ROCs) are presented for a detection scenario involving a variety of unknown object and background parameters. A computationally efficient, hard-limiter matched-filter processor is shown to yield performance closely approximating that of the GLRT.
Maximum-likelihood range profiling is considered for pulse-dimager operation of a coherent laser radar. In particular, the expectation-maximization algorithm is used to develop a computationally simple procedure for fitting a planar surface to laser radar range data. Basic analytic properties of the algorithm are reviewed and results based on simulated and real range data are presented.
Target detection theory is developed for 3-D pulsed imager operation of a coherent laser radar in a downlooking scenario. Generalized likelihood-ratio tests (GLRTs) and receiver operating characteristics (ROCs) are presented for range-only and joint-range-intensity processors. This work extends previous studies in three ways: (1) fine-range information is included; (2) maximum-likelihood estimation of an unknown range plane is performed; and (3) connections to Markov random field preprocessing are established.
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