Adaptive data fusion using finite-set statistics

Adel I. El-Fallah; Ronald P. S. Mahler; Ravi B. Ravichandran; Raman K. Mehra

doi:10.1117/12.357195

27 July 1999 Adaptive data fusion using finite-set statistics

Adel I. El-Fallah, Ronald P. S. Mahler, Ravi B. Ravichandran, Raman K. Mehra

Proceedings Volume 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII; (1999) https://doi.org/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.

Citation Download Citation

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); https://doi.org/10.1117/12.357195

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available