Modified Unscented Recursive Nonlinear Dynamic Data Reconciliation for Constrained State Estimation

In state estimation problems, often, the true states satisfy certain constraints that need to be incorporated during the estimation procedure. Amongst various constrained nonlinear state estimation algorithms proposed in literature, the unscented recursive nonlinear dynamic data reconciliation (URNDDR) proposed by Vachhani et al. ( 2006) seems to be promising since it is able to incorporate constraints while maintaining the recursive nature of estimation. In this article, we propose a modified URNDDR algorithm that gives superior performance compared to basic URNDDR. The improvements are obtained via better constraint handling and are demonstrated via a representative case study.