An Improved Particle Filter Algorithm Based on Neural Network for Target Tracking
To the shortcoming of general particle filter, an improved algorithm based on neural network is proposed and is shown to be more efficient than the general algorithm in the same sample size. The improved algorithm has mainly optimized the choice of importance density. After receiving the samples drawn from prior density, and then adjust the samples with general regression neural network (GRNN), make them approximate the importance density. Apply the new method to target tracking problem, has made the result more precise than the general particle filter.
Key wordsparticle filter target tracking general regression neural network
- 2.A. Doucet. On sequential Monte Carlo methods for Bayesian filtering. Dept. Eng., Univ. Cambridge, UK, Tech. Rep., 1998.Google Scholar
- 3.R. van der Merwe, A. Doucet, N, de Freitas, E. Wan. The Unscented Particle Filter. Adv. Neural Inform. Process. Syst., Dec.2000Google Scholar
- 4.Yuan Zejian, Zheng Nanning, Jia Xinchun. The Gauss-Hermite Particle Filter. Dian Zi Xue Bao, 2003, 31(7):970–973Google Scholar
- 5.Peter Torma, Csaba Szepesvari. LS-N-IPS:an improvement of particle filters by means of local search. In Proc. Non-Linear Control Systems (NOLCOS’01), 2001Google Scholar