A unified approach to state estimation problems under data and model uncertainties

We present a unified approach to the problem of state estimation under measurement and model uncertainties. The approach allows formulation of problems such as maneuvering target tracking, target tracking in clutter, and multiple target tracking using a single state-space system with random matrix coefficients. Consequently, all may be solved efficiently using a single IMM algorithm or using a linear optimal filter, derived elsewhere, thus replacing the need for deriving a unique algorithm for each problem.