Nonlinear Non-Gaussian Filtering Algorithm Based on Cubature Kalman and Particle Filter

Abstract:

To resolve the nonlinear non-Gaussian tracking problem effectively, a novel filtering algorithm based on Cubature Kalman Filter (CKF) and Particle Filters (PF) is proposed, which is called Cubature Kalman Particle Filter (CPF). CKF is used to generate the importance density function for PF. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian cubature points. It need not compute the Jacobian matrix. Moreover, it makes efficient use of the latest observation information into system state transition density, thus greatly improving the filter performance. The simulation results show that CPF has higher estimation accuracy and less computational load comparing against the widely used Unscented Particle Filter (UPF).

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 1323-1326
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.1323
• Online since: August 2013
• Authors: Zhang Kai, Gan Lin Shan
• Keywords: Cubature Kalman Filter, Nonlinear Non-Gaussian, Particle Filter (PF)

References

[1] M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesial Tracking [J]. IEEE Trans. AES, 2002 (2) 174-188.

DOI: 10.1109/78.978374

[2] N.J. Gordon, D.J. Salmond, A.F.M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation [C]. IEEE Proc. Inst. Elect. Eng. F, 1993. 107-113.

DOI: 10.1049/ip-f-2.1993.0015

[3] R. vander Merwe, A. Doucet, N. de Freitas, et al. The unscented particle filter. Technical Report of the Cambridge University Engineering Department, UK: Cambridge University Press, (2000).

[4] S.J. Julier, J.K. Uhlmann. Unscented filtering and nonlinear estimation [C]. Proc. of the IEEE, 2004. 401-422.

DOI: 10.1109/jproc.2003.823141

[5] L. Smith, V. Aitken. The Auxiliay Extended and Auxiliary Unscented Kalman Particle Filters [C]. Canadian Conference on Electronical and Computer Engineering, 2007. 107-113.

DOI: 10.1109/ccece.2007.407

[6] D. Guo, Wang X., Chen R. New sequential monte carlo methods for nonlinear dynamic systems [J]. Statistics and Computing, 2005 (2) 135-147.

DOI: 10.1007/s11222-005-6846-5

[7] Chunling. Wu, Chongzhao. Han. Quadrature Kalman particle filter [J]. Journal of Systems Engineering and Electronics, 2010 (2) 175-179.

[8] Wenyan Guo, Chongzhao Han, Ming lei. Improved unscented particle filter for nonlinear bayesian estimation [C]. Proceeddings of the 10th Interna-tional Conference on Information Fusion, 2007. 1-6.

DOI: 10.1109/icif.2007.4407986

[9] I. Arasaratnam, S. Haykin. Cubature Kalman filters [J]. IEEE Trans. Automatic Control, 2009 (6) 1254-1269.

DOI: 10.1109/tac.2009.2019800

[10] Mingjie Wan, Pengfei Li, Tao Li. Tracking Maneuvering Target with Angle-Only Measurements Using IMM Algorithm Based on CKF [C]. IEEE International Conference On Communications and Mobile Computing, 2010. 92-96.

DOI: 10.1109/cmc.2010.239

[11] Deok-Jin LEE. Nonlinear Bayesian Filtering with Applications to Estimation and Navigation, the dissertation of Ph. D, (2005).

[12] Doucet A., De. Freitas N, Gordon N. J. Sequential Monte Carlo Methods in Practice. Springer: New York, (2001).