ABSTRACT
This chapter describes an important, practical, widely studied application of the distributed estimation theories. It discusses representative track fusion rules, and numerically compares the performance, under a set of prescribed variations of track fusion environments and designs. The chapter examines a simple one-time track-to-track association and compares the performance using various track-to-track association metrics. Some of the track fusion rules described subsequently can be used for track fusion problems with nonlinear target dynamics and nonlinear observation models. Track association is a prerequisite for track fusion in a distributed tracking system. Association is rather obvious, and therefore target state estimation from multiple sensors or track fusion becomes the major problem. The chapter addresses the track fusion and association problems in distributed multiple-target tracking. It reviews several track fusion algorithms developed over the last three decades and compared their performance. The use of linear-Gaussian models allows closed form analytical performance evaluation.
