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26 April 2010 Adaptive tracking in sensor data fusion
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In the design of a target track processor, specific models and parameters are selected for use in the processing of the sensor data. Some of the parameters may be known and some are not known with certainty so estimates of the values are used. The values of some of the design parameters may be significantly in error and so adjustments are needed. In the field of Kalman filters for example, adaptive methods (sometimes called adaptive filters) have been implemented to estimate in real time (sometimes slowly) more appropriate values for some of the design parameters. While existing adaptive filter methods might be considered for use in estimating some of the parameters in tracking, the possibility of misassociations of measurements to tracks introduces substantial anomalies that may require alternative adaptive methods (possibly more complex). Tracking with data from multiple sensors as in sensor data fusion introduces both additional advantages and challenges for adaptive methods relative to single sensor tracking. Fusion also involves functions beyond those typical of tracking and many fusion functions depend on the estimated tracks and/or influence the tracking performance. To date, most tracker design efforts have been directed toward improving the accuracy of the estimated target state using fixed design parameters (and models) and little effort has been directed to real time adaptive processing to improving the estimates of the design parameters. This paper addresses the degradation in tracker and fusion performance caused by inaccurate design parameters and addresses some considerations for adaptive tracking in sensor data fusion.
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Oliver E. Drummond "Adaptive tracking in sensor data fusion", Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 769810 (26 April 2010);


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