Radar Centroid Processing Algorithm with Tracker Feedback

The raw detection data provided by a radar typically consists of primitive measurements that correspond to discrete range-bearing cells. A single target can generate multiple primitive detections in adjacent range-bearing cells as the radar beam scans across it; the radar system therefore employs a centroid processing algorithm to combine the primitive measurements and form a single merged detection report to pass along to the tracker. However, if multiple targets exist close to one another, this clustering process can produce undesirable results such as track degradation and/or track drop, since the measurements for more than one target may be combined to produce a single report. Here, we propose a method for utilizing feedback from the tracker to produce the appropriate number of centroid estimates in a given area, thus avoiding track drop. Our method is a modification of the Expectation-Maximization algorithm as applied to the Gaussian Mixture Estimation problem; accordingly, we will refer to our algorithm as EMARCT (an Expectation Maximization Algorithm for Radar Centroid processing with Tracker feedback). In Phase I we will develop the algorithm in greater detail, program it in a high-level language such as matlab, and demonstrate its feasibility on simulated data.