Title :
Gaussian-sum cubature Kalman smoothers for bearings-only tracking
Author :
Pei Hua Leong ; Arulampalam, Sanjeev ; Lamahewa, Tharaka A. ; Abhayapala, Thushara D.
Author_Institution :
Res. Sch. of Eng., Australian Nat. Univ. (ANU), Canberra, ACT, Australia
Abstract :
In this paper, a fixed-lag and a fixed-interval Gaussian-sum cubature Kalman smoother are proposed for the bearings-only tracking problem. The smoothers are of the forward-backward type and they utilise the Gaussian-sum cubature Kalman filter with improved robustness presented by the authors in [1]. Simulation results show that both the fixed-lag and fixed-interval smoothers exhibit improved accuracy over their filtering counterpart and outperform other existing smoothers of the same type for this problem, with the root-mean-square error overlapping the Cramér-Rao lower bound.
Keywords :
Gaussian processes; Kalman filters; direction-of-arrival estimation; least mean squares methods; smoothing methods; target tracking; Cramer-Rao lower bound; Gaussian-sum Cubature Kalman smoother; bearings-only tracking problem; fixed interval smoother; fixed lag smoother; root mean square error overlapping; Accuracy; Bayes methods; Covariance matrices; Kalman filters; Noise measurement; Smoothing methods; Vectors;
Conference_Titel :
Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014 IEEE Ninth International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4799-2842-2
DOI :
10.1109/ISSNIP.2014.6827589