Neural Network Schemes for Data Fusion and Tracking of Maneuvering Targets
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Published: 1996
Total Pages: 47
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DOWNLOAD EBOOKThis is a six-monthly performance report on the ONR sponsored Project 'Neural Network Schemes for Data Fusion and Tracking of Maneuvering Targets.' This is a new project at the University of Arizona and work on this project was commenced in August 1995. In this report we outline the current status of this project and the work accomplished during the first six months after the project start date. The ability to efficiently fuse information of different forms for facilitating intelligent decision-making is one of the major capabilities of trained multilayer neural networks that is being recognized in the recent times. While development of innovative adaptive control algorithms for nonlinear dynamical plants which attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. 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. 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.
