Distributed (Nonlinear) Target Tracking in Clutter

Abstract

We consider target tracking using a network of sensors, where some (or all) sensors may be nonlinear or even nonobservable. With nonlinear/nonobservable sensors we may locally track the measurement state. We show that increasing the local measurement state derivative (partially) compensates the nonlinearity of the local measurement state propagation. For example, given a uniform target motion, one should also track the measurement state acceleration. We also simplify the distributed fusion by avoiding the explicit track-to-track (T2T) association. The equivalent measurements (EMs) are used to both update all existing and initialize new global tracks. This approach allows distributed false track discrimination (FTD) and trajectory estimation. We apply this material to distributed tracking in clutter using the time/frequency difference of arrival (TFDOA) sensors.