Projection-based state estimation using noisy destination

The Journal of Engineering

Keyi Li¹  Gongjian Zhou¹  Chang Zhou¹ 

[1] Harbin Institute of Technology;

关键词: kalman filters;    nonlinear filters;    monte carlo methods;    state estimation;    estimation system;    estimation accuracy;    state estimation method;    unscented kalman filter;    unconstrained estimation;    taylor series expansion;    destination constraint;    projection method;    unconstraint estimate;    constraint surface;    projection-based state estimation;    noisy destination;    noisy prior information;    constraint information;   

DOI  :  10.1049/joe.2019.0728

来源: DOAJ

【 摘 要 】

The problem of state estimation with a destination constraint using the noisy prior information of the destination is investigated. With the utilisation of constraint information in estimation system, the estimation accuracy can be significantly enhanced. A projection-based constrained state estimation method is proposed to address this problem. In this method, the unscented Kalman filter is employed to obtain the unconstrained estimation. The Taylor series expansion is adopted to deal with the non-linearity of the destination constraint and the projection method is used to project the unconstraint estimate onto the constraint surface. Monte Carlo simulation results are presented to illustrate the effectiveness of the new approach.

【 授权许可】

Unknown