Neural Network Schemes for Data Fusion and Tracking of Maneuvering Targets

摘要

In this report, we outline the current status of this project and the workaccomplished during the first six months after the project start date. In this report, we 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. 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.