Detecting and tracking low-observable targets using IR

Abstract

Sensitivity to low—observable targets—-given a sequence of N frames of pre—processed (e.g., spatial/time-filtered) but unthresholded data—may be enhanced by selecting, from among all possible paths traversing these frames, those containing any indication that a target may be present. However, since only upper bounds regarding target velocities are usually known, explicit formulation of all feasible paths (and accompanying confidence factors) becomes a formidable task even for small values of N. In this paper we address this problem by utilizing a Dynamic Programming ("Viterbi") algorithm to efficiently generate and evaluate, in an unthresholded fashion, all possible paths through the N frames. Trajectories are traced recursively by assigning accumulated trajectory scores to each entry in a given frame of data so as to maximize that entry's updated score. This Viterbi Track- Before—Detect procedure differs from standard Multiple Hypothesis Testing (MHT) methods in two ways. First, while in the MHT method the number of plausible paths grows exponentially (hence the need for introducing thresholds), in the Viterbi approach they remain constant, equal to the number of data entries in a frame. Second, whereas in the MHT method trajectories are updated by selecting for each existing trajectory the best current (thresholded) detection, in the Viterbi approach each current data value is updated with the best trajectory up to that point. Simulation results show that application of the Viterbi Track-Before-Detect algorithm over ten frames of IR data yields roughly a 7 dB improvement in detection sensitivity over conventional thresholding/peak-detection procedures.