Clutter Rejection using Peak Curvature

A typical tracking algorithm takes its input from a peak detector or plot extractor. This process reduces the sensor image data to point measurements and reduces the volume of data that the tracker must process. However, useful information can be lost. This paper shows how the clutter of a peak can be a useful feature for discriminating false alarms and valid detections. The benefit obtained by using this feature is quantified through false track rate on recorded sensor data. On recorded data with difficult clutter conditions, approximately sixty percent of false tracks are rejected by exploiting peak curvature