Signal Detection Theory Approach to the Multiple Parallel Moving Targets Problem

The problem of detection and line location estimation of multiple, parallel, dim, moving targets, such as the ones typically encountered when a geostationary satellite is tracking targets, is studied under the framework of signal detection theory. Part I of the paper considers two-dimensional data (or one frame of an image) and Part II considers an additional third dimension representing time. Optimal processors are derived for varying degrees of uncertainty in the data for the detection of parallel targets. The uncertainties include uncertainty in the knowledge of orientation, location, number, and direction of arrival of the targets. Performance of the optimal processors is presented in the form of Receiver Operating Characteristic (ROC) curves and compared, in Part I, with the Hough transform. The optimal 2-D processors perform better than the Hough transform under all cases of uncertainties. Likelihood-ratio-based optimal estimation algorithms resolve the location of targets under severe noise conditions. In Part II, ROCs for the optimal 3-D processors are compared with both 2-D optimal processors and the Hough transform that use the projected data. Simulation results indicate that substantial gains in performance can be achieved by processing the 3-D data directly instead of first projecting and optimally processing in 2-D. It is observed that the computational burden in optimally processing the 3-D data sequentially is comparable to the conventional techniques involving projection and the Hough transform.