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A simplex trained neural network-based architecture for sensor fusion and tracking of target maneuvers. (English) Zbl 1274.93265
Summary: One of the major applications for which neural network-based methods are being successfully employed is the design of intelligent integrated processing architectures that efficiently implement sensor fusion operations. In this paper, we shall present a novel scheme for developing fused decisions for surveillance and tracking in typical multi-sensor environments characterized by the disparity in the data streams arriving from various sensors. This scheme employs an integration of a multilayer neural network trained with features extracted from the multi-sensor data and a Kalman filter in order to permit reliable tracking of maneuvering targets, and provides an intelligent way of implementing without any attendant increases in computational complexity. A particular focus is given to optimizing the neural network architecture and the learning strategy which are particularly critical to develop the capabilities required for tracking of target maneuvers. Towards these goals, a network growing scheme and a simplex algorithm that seeks the global minimum of the training error are presented. To provide validation of these methods, results of several tracking experiments involving targets executing complex maneuvers in noisy and clutter environments are presented.

MSC:
93E11 Filtering in stochastic control theory
92B20 Neural networks for/in biological studies, artificial life and related topics
68M10 Network design and communication in computer systems
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