27 July 1999 Adaptive data fusion using finite-set statistics
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Proceedings Volume 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII; (1999); doi: 10.1117/12.357195
Event: AeroSense '99, 1999, Orlando, FL, United States
Abstract
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.
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Adel I. El-Fallah, Ronald P. S. Mahler, Ravi B. Ravichandran, Raman K. Mehra, "Adaptive data fusion using finite-set statistics", Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357195; https://doi.org/10.1117/12.357195
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KEYWORDS
Sensors

Fuzzy logic

Data fusion

Detection and tracking algorithms

Synthetic aperture radar

Data modeling

Nonlinear filtering

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