Nonlinear distributed tracking with probabilistic data association
Ali T. Alouani · 2003
A nonlinear distributed tracking technique using probabilistic data association to account for the uncertainty in the origin of the measurement data is presented. In this distributed tracking structure there is a coordinator which contains the model of a nonlinear random process (state of the target in track), and an arbitrary number, N, of spatially distributed sensors taking observations of the target's state. The coordinator must reconstruct the (global) probability density of the target's state conditioned on the observation histories of the sensors. However, the coordinator can only access the N (local) conditional densities produced by local processing of the sensor's returns, not the observation histories themselves.>