Multiple importance unscented Kalman filtering with soft spatiotemporal constraint for multi‐passive‐sensor target tracking.

Multi‐passive‐sensor systems are a common means for the target tracking and their bearings processing is a prerequisite for stable control and nonlinear filtering. This study proposes a mathematical methodology that is based on the incorporating deterministic unscented transition rules into stochastic sequential importance sampling frame and makes use of soft spatiotemporal constraint comprise multiview epipolar geometry constraint and numerical regularization to solve the correspondence problem. A prototype measurement‐driven target tracking frame was developed that can work in real time and achieve filtering improvements of 41%–46% and 43%–48% in terms of root‐mean‐square error and root time‐averaged mean square error compared with the state‐of‐the‐art multiple model Rao–Blackwell particle filtering method, as proven by the simulation results. [ABSTRACT FROM AUTHOR]