Radar target tracking problem has been a popular topic for several decades. Recent works have shown that the performance of tracking algorithms increases as more prior information is used by the system / such as maximum velocity and maximum acceleration of the target, altitude of the target, or the elevation structure of the terrain. In this thesis we will focus on increasing the performance of tracking algorithms making use of benefit from the elevation model of the environment where the target tracker is searching. For a constant target altitude and a certain radar location, we generate a &ldquo / visibility map&rdquo / using the elevation model of the terrain and use this information to estimate the location and the time that the target will reappear. The second aim of this work is to use the visibility map information for improving the performance of track initiation. For that purpose, a special map has been formed, that we call as the &ldquo / track initiation probability map&rdquo / , which shows the target first time appearance density. This information has been used at the initialization part of the track initiation algorithm in order to increase the performance.

ABSTRACT EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER G&uuml / ner, Onur M.S., Department of Electrical and Electronics Engineering Supervisor: Prof. Dr. Mustafa Kuzuoglu August 2005, 88 Pages This thesis describes the theoretical bases, implementation and testing of a multi target tracking approach in radar applications. The main concern in this thesis is the evaluation of the performance of tracking algorithms in the presence of false alarms due to clutter. Multi target tracking algorithms are composed of three main parts: track initiation, data association and estimation. Two methods are proposed for track initiation in this work. First one is the track score function followed by a threshold comparison and the second one is the 2/2 &amp / M/N method which is based on the number of detections. For data association problem, several algorithms are developed according to the environment and number of tracks that are of interest. The simplest method for data association is the nearest-neighbor data association technique. In addition, the methods that use multiple hypotheses like probabilistic data association and joint probabilistic data association are introduced and investigated. Moreover, in the observation to track assignment, gating is an important issue since it reduces the complexity of the computations. Generally, ellipsoidal gates are used for this purpose. For estimation, Kalman filters are used for state prediction and measurement update. In filtering, target kinematics is an important point for the modeling. Therefore, Kalman filters based on different target kinematic models are run in parallel and the outputs of filters are combined to yield a single solution. This method is developed for maneuvering targets and is called interactive multiple modeling (IMM). All these algorithms are integrated to form a multi target tracker that works in the presence (or absence) of clutter. Track score function, joint probabilistic data association (JPDAF) and interactive multiple model filtering are used for this purpose. Keywords: clutter, false alarms, track initiation, data association, gating, target kinematics, IMM, JPDAF