Author(s)

Hongxiang Dai, Li Zou

Department of Statistics, Jinan University, Guangzhou, China.

In algorithms of nonlinear Kalman filter, the so-called extended Kalman filter algorithm actually uses first-order Taylor expansion approach to transform a nonlinear system into a linear system. It is obvious that this algorithm will bring some systematic deviations because of ignoring nonlinearity of the system. This paper presents two extended Kalman filter algorithms for nonlinear systems, called second-order nonlinear Kalman particle filter algorithms, by means of second-order Taylor expansion and linearization approximation, and correspondingly two recursive formulas are derived. A simulation example is given to illustrate the effectiveness of two algorithms. It is shown that the extended Kalman particle filter algorithm based on second-order Taylor expansion has a more satisfactory performance in reducing systematic deviations and running time in comparison with the extended Kalman filter algorithm and the other second-order nonlinear Kalman particle filter algorithm.

Kalman Particle Filter, Nonlinear System, Taylor Expansion, Linearization Approximation

Dai, H. and Zou, L. (2015) Two Second-Order Nonlinear Extended Kalman Particle Filter Algorithms. Open Journal of Statistics, 5, 254-261. doi: 10.4236/ojs.2015.54027.