ABSTRACT
This chapter provides an introductory examination of radar tracking while highlighting important concepts and explaining mature techniques. It is intended to provide the reader a foundation on which additional learning can be applied. Radar tracking is the estimation and maintenance of target parameters that can be measured or derived by the radar, such as range and angle. The accuracy of the measurements themselves can be improved by combining received signals as to drive out systemic errors. Monopulse is one of these well known techniques. The two main functions to be performed in radar tracking are data association and filtering. The first is an attempt to assign incoming detections to established tracks are create new ones. Data association techniques range from the simple, such as Nearest Neighbor, to extremely complicated, such as Multiple Hypothesis Tracking. Track filtering is an attempt to improve the estimate by removing measurement noise. The workhorse of track filtering is the Kalman Filter, which has its roots in least squares estimation. The Kalman filter is extremely versatile and can be adapted to nonlinear target dynamics or measurements, and can be included in multiple filter implementations.
