State estimation of nonlinear dynamic system using novel heuristic filter based on genetic algorithm
- 82 Downloads
This paper introduces a new filter for nonlinear systems state estimation. The new filter formulates the state estimation problem as a stochastic dynamic optimization problem and utilizes a new stochastic method based on genetic algorithm to find and track the best estimation. In the proposed filter, each individual is set based on stochastic selection and multiple mutations to find the best estimation at every time step. The population searches the state space dynamically in a similar scheme to the optimization algorithm. This approach is applied to estimate the state of some nonlinear dynamic systems with noisy measurement and its performance is compared with other filters. The results indicate an improved performance of heuristic filters relatives to classic versions. Comparison of the results to those of extend Kalman filter, unscented Kalman filter, particle filter and heuristic filters indicated that the proposed heuristic filter called genetic filter fulfills the essential requirements of fast and accuracy for nonlinear state estimation.
KeywordsState estimation Nonlinear system Genetic algorithm Genetic filter Heuristic filter
Compliance with ethical standards
Conflict of interest
The authors have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Andrew HJ (1970) Stochastic processes and filtering theory. In: Mathematics in science and engineering, vol 64. Academic Press, Inc LondonGoogle Scholar
- Bucy RS (1969) Bayes theorem and digital realizations for non-linear filters. J Astronaut Sci 17:80Google Scholar
- Carpenter J, Clifford P, Fearnhead P (1999) Improved particle filter for nonlinear problems. In: IEE proceedings radar, sonar and navigation. IET, pp 2–7Google Scholar
- Clapp TC (2001) Statistical methods for the processing of communications data. Doctoral dissertation, University of CambridgeGoogle Scholar
- Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEE proceedings F radar and signal processing. IET, pp 107–113Google Scholar
- Hao Z, Zhang X, Yu P, Li H (2010) Video object tracing based on particle filter with ant colony optimization. In: 2010 2nd international conference on advanced computer control (ICACC). IEEE, pp 232–236Google Scholar
- Julier SJ, Uhlmann JK (1997) New extension of the Kalman filter to nonlinear systems. In: AeroSense’97. International Society for Optics and Photonics, pp 182–193Google Scholar
- Kim Y-S, Hong K-S (2004) An IMM algorithm for tracking maneuvering vehicles in an adaptive cruise control environment. Int J Control Autom Syst 2:310–318Google Scholar
- Nobahari H, Sharifi A (2012) A novel heuristic filter based on ant colony optimization for non-linear systems state estimation. In: Computational intelligence and intelligent systems. Springer, pp 20–29Google Scholar
- Pourtakdoust SH, Nobahari H (2004) An extension of ant colony system to continuous optimization problems. In: International workshop on ant colony optimization and swarm intelligence. Springer, Berlin, pp 294–301Google Scholar
- Siouris GM (1996) An engineering approach to optimal control and estimation theory. Wiley, New YorkGoogle Scholar
- Smith A, Doucet A, de Freitas N, Gordon N (2013) Sequential Monte Carlo methods in practice. Springer, BerlinGoogle Scholar
- Sorenson HW (1988) Recursive estimation for nonlinear dynamic systems. Bayesian Anal Time Ser Dyn Model 94:127–165Google Scholar
- Tong G, Fang Z, Xu X (2006) A particle swarm optimized particle filter for nonlinear system state estimation. In: IEEE congress on evolutionary computation, 2006. CEC 2006. IEEE, pp 438–442Google Scholar
- Uosaki K, Kimura Y, Hatanaka T (2003) Nonlinear state estimation by evolution strategies based particle filters. In: The 2003 congress on evolutionary computation, 2003. CEC’03. IEEE, pp 2102–2109Google Scholar
- Zandavi SM, Sha F, Chung V, Lu Z, Zhi W (2017) A novel ant colony detection using multi-region histogram for object tracking. In: International conference on neural information processing. Springer, Cham, pp 25–33Google Scholar