Thomas J. Green

Solid-state laser synthetic aperture radar

Here we report the operation of an optical synthetic aperture radar employing, for the first time to our knowledge, a solid-state laser as the source. The experimental data-acquisition and digital processing techniques are described, by which spatial resolution superior to that limited by diffraction is demonstrated.

Synthetic-aperture-radar imaging with a solid-state laser

We report the operation of an imaging Nd:YAG microchip-laser synthetic-aperture radar, with which we imaged two-dimensional (2-D) models of military targets. The images obtained showed spatial resolution significantly better than the diffraction limit of the real aperture in the along-track dimension. The signal processing is described, and the measurement sensitivity is both predicted and verified. In addition, 2-D images with high resolution in both dimensions were generated by using an asymmetric aperture to match the along-track synthetic-aperture resolution with the across-track diffraction-limited resolution.

Detecting objects in three-dimensional laser radar range images

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 laser radar range profiling with the expectation-maximization algorithm

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.

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) 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.

Target detection performance using 3-D laser radar images

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.

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 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.

On the performance of the order-truncate-average-ratio spectral filter

We describe a nonlinear processor, called the order-truncate-average-ratio spectral filter, that is designed to simultaneously whiten broadband background, preserve intermediate bandwidth features and suppress narrowband interference.

Target recognition theory for laser radar imaging

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.