Multisensor data fusion for manoeuvring target tracking

The aim of this paper is to describe an approach that performs data fusion on the output of the multiple sensors engaged in the manoeuvre target track ing. A common approach is to use the extended Kalman filter (EKF) for manoe uvre tracking problems, and the probabilistic data association (PDA) filter was adopted for the multisensor case. However, certain assumptions made in the derivation of the EKF algorithms render it suboptimal for track estima tion. An efficient tracker that can use data from a host of sensing modalit ies and are capable of reliably tracking even a target may accelerate at no n-uniform rates and may also complete sharp turns within a short time perio d. Further, the target may be missing from successive scans during the turn s. A tracker incorporating radial basis function (RBF) network in a convent ional EKF-PDA tracker is proposed, which has several advantages over existi ng nonlinear estimation algorithms in tracking applications. The main advan tage is to gain the capability of adaptability and robustness from the RBF network in order to realize improved tracking performance while at the same time keeping the data fusion computational structure of the tracker as sim ple as possible.