Projection-based state estimation using noisy destination

Abstract

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.

Keywords

• 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