Naslov
Unified Robust-Bayes Multisource Ambiguous Data Rule Fusion

Autori
El-Fallah, Adel ; Zatezalo, Aleksandar ; Mahler, Ronald ; Mehra, K. Raman

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of SPIE / Kadar, Ivan - Bellingham (WA) : SPIE, 2005

Skup
Defense and Security Symposium 2005

Mjesto i datum
Orlando (FL), Sjedinjene Američke Države, 28.03.2005

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Bayes Filtering; Fuzzy Logic; Random Sets; Rules Fusion; Ambiguous Data

Sažetak
The ambiguousness of human information sources and of a PRIORI human context would seem to automatically preclude the feasibility of a Bayesian approach to information fusion. We show that this is not necessarily the case, and that one can model the ambiguities associated with defining a “ state” or “ states of interest” of an entity. We show likewise that we can model information such as natural-language statements, and hedge against the uncertainties associated with the modeling process. Likewise a likelihood can be created that hedges against the inherent uncertainties in information generation and collection including the uncertainties created by the passage of time between information collections. As with the processing of conventional sensor information, we use the Bayes filter to produce posterior distributions from which we could extract estimates not only of the states, but also estimates of the reliability of those state-estimates. Results of testing this novel Bayes-filter information-fusion approach against simulated data are presented.

Izvorni jezik
Engleski

Znanstvena područja
Matematika

POVEZANOST RADA