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Surveillance tracking is rapidly becoming an important application for GMTI radars. Surveillance tracking differs from precision tracking primarily in the scope of the problem being considered. Where precision tracking focuses primarily on the highly accurate location of a few numbers of targets, surveillance tracking is more interested in understanding the general location of large numbers of targets.
Several challenges arise as one attempts to directly apply techniques from precision tracking applications to the surveillance realm. In the surveillance problem using GMTI radars, the revisit rate is typically lower due to a larger area that must be considered. As a result, in all but the most benign environments, it is difficult to generate estimates of individual targets. This challenge is further compounded by poor sensor performance which results in large uncertainty in target positions and ambiguity from closely spaced targets. Given these issues, new techniques are required for addressing the surveillance tracking problem.
Our approach is to treat the surveillance problem in a slightly different fashion. Rather than attempting to track each of the individual targets in the surveillance region, we will focus on the bigger picture and track the groups of targets. This generalization will result in measurement not being individual radar returns off of a single target but rather a clustered grouping of detections representing a single group detection with both a location and group size. In this way, we are able to provide a true group tracking solution rather than attempting cluster the tracks of individual targets. In order to perform this task it is necessary to cluster detections of targets into group measurements, estimate the size of the group, and to provide an estimate of the location of the group. This paper will describe alternative approaches for clustering of detections and an examination of their performance in the overall group tracking approach. Additionally we will describe a technique for estimating the size of the group using knowledge of sensor performance characteristics and the number of detections that are clustered together. Finally we will describe a method for generating a reasonable estimate of the location of the group. We will conclude the paper with an example that examines the overall system performance on a representative problem.
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