Data fusion and nonlinear tracking filter implementation using multilayer networks

One of the capabilities of multilayer neural nets that has not received much attention is the ability to efficiently fuse information of different forms for facilitating intelligent decision-making. In this paper the authors describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the currently popular efforts at designing nonlinear estimation algorithms for tracking applications, the principal one being the reduction of mathematical and computational complexities