Title: Cramér-Rao Lower Bound for State-Constrained Nonlinear Filtering
Abstract:This letter presents a mean-square error lower bound for state estimation of nonlinear stochastic systems under given differentiable state constraints. Its recursive formulation permits incorporation ...This letter presents a mean-square error lower bound for state estimation of nonlinear stochastic systems under given differentiable state constraints. Its recursive formulation permits incorporation of random process and measurement errors and is shown to be a generalization of the known lower bound for unconstrained problems. The bound is evaluated for the example of locating a ground vehicle from noisy measurements of its horizontal position and velocity incorporating a roadmap.Read More
Publication Year: 2017
Publication Date: 2017-10-19
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 7
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot