ESTIMATION USING SUBJECTIVE KNOWLEDGE WITH TRACKING APPLICATIONS

Within the framework of classical estimation theory, there is no techn ically legitimate way to utilize knowledge that cannot be codified by either deterministic or strict probability models. We introduce a theo retically defensible approach (coordinated objective/subjective estima tion (COSE)) for the simultaneous incorporation of both objective and subjective knowledge in estimation. Also discussed is a technique (heu ristically constrained estimation (HCE)) which is a particular interpr etation of the Bayesian use of subjective priors. We apply COSF, HCE, and classical maximum a posteriori probability (MAP) estimation to a t racking problem.