A unified approach to state estimation problems under data and model uncertainties
Daniel Sigalov, Tomer Michaeli, Yaakov Oshman · 2012
Abstract—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. Index Terms—Maneuvering target tracking, clutter and data association, hybrid systems, multiple target tracking I.