Abstract:
In this letter, a new method termed as new sigma point Kalman filter, is proposed for generating sigma points and weights for estimating the states of a stochastic nonlin...Show MoreMetadata
Abstract:
In this letter, a new method termed as new sigma point Kalman filter, is proposed for generating sigma points and weights for estimating the states of a stochastic nonlinear dynamic system. The sigma points and their corresponding weights are generated such that the points nearer to the mean (in inner product sense) have a higher probability of occurrence, and the mean vector and covariance matrix are matched exactly. Performance of the new algorithm is compared with the existing unscented Kalman filter (UKF), the cubature Kalman filter (CKF), the cubature quadrature Kalman filter (CQKF) and higher order unscented filter (HOUF) for two different problems. Comparison is done by calculating the root mean square error, relative computational time and trackloss. From simulation results, it can be concluded that the proposed algorithm performs with superior estimation accuracy when compared to the UKF, CKF, CQKF and HOUF.
Published in: IEEE Control Systems Letters ( Volume: 2, Issue: 3, July 2018)