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27 July 1999 Adaptive data fusion using finite-set statistics
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Real-time fusion algorithms are often patchworks of loosely integrated sub-algorithms, each of which addresses a separate fusion objective and each of which may process only one kind of evidence. Because these objectives are often in conflict, adaptive methods (e.g. internal monitoring and feedback control to dynamically reconfigure algorithms) are often necessary to ensure optimal performance. This paper describes a different approach to adaptive fusion in which explicit algorithm reconfiguration is largely unnecessary because conflicting objectives are simultaneously resolved within a self-reconfiguring, optimally integrated algorithm. This approach is based on Finite-Set Statistics (FISST), a special case of random set theory that unifies many aspects of multisource-multitarget data fusion, including detection, tracking, identification, and evidence accrual. This paper describes preliminary results in applying a FISST-based filtering approach to a ground-based, single-target identification scenario based on the fusion of several types of synthetic message-based data from several sensors.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Adel I. El-Fallah, Ronald P. S. Mahler, Ravi B. Ravichandran, and Raman K. Mehra "Adaptive data fusion using finite-set statistics", Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999);

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