Unified evidence accrual for SAR: recent results
Melvyn Huff, Ssu-Hsin Yu, Ronald Mahler, Bharadwaj Ravichandran, Raman K. Mehra, Stanton Musick · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
During the last two decades IR Goodman, HT Nguyen and others have shown that several basic aspects of expert-systems theory-fuzzy logic, Dempster-Shafer evidence theory, and rule-based inference-can be subsumed within a completely probabilistic framework based on random set theory. In addition, it has been shown that this body of research can be rigorously integrated with multisensor, multitarget filtering and estimation using a special case of random set theory called 'finite-set statistics' (FISST). In particular, FISST allows the basis for standard tracking and ID algorithms-nonlinear filtering theory and estimation theory; to be extended to the case when evidence can be highly 'ambiguous' because of extended operating conditions, e.g. when images are corrupted by effects such as dents, mud etc. This paper extends those results by showing that the technique is relatively insensitive to the uncertainty model used to construct the ambiguous likelihood function.