Unscented Particle Filter Using Gaussian Particle Swarm Optimization

doi:10.13474/j.cnki.11-2246.2017.0107

Abstract

Abstract: A new unscented particle filter using Gaussian particle swarm optimization (GPSO-UPF) algorithm is proposed in this paper to improve particle degeneracy and particle impoverishment. It uses unscented particle filter in importance sampling process and incorporates Gaussian particle swarm optimization into re-sampling process. Through GPSO-UPF, the probability density moves closely to true state, the number of effective particles and efficiency are increased, the particle degeneracy is reduced and particle impoverishment is relieved. The experimental results show that the state estimation precision of GPSO-UPF is higher than estimation precision of PF and UPF.

Key words: unscented particle filter, Gaussian particle swarm optimization, particle degeneracy, particle impoverishment

CLC Number:

P207

WANG Jing, NIE Guigen, XUE Changhu. Unscented Particle Filter Using Gaussian Particle Swarm Optimization[J]. 测绘通报, 2017, 0(4): 1-5.

References

[1] MERWE R V D, DOUCET A,FREITAS N D, et al. The Unscented Particle Filter[J]. Advances in Neural Information Processing Systems, 2001, 13:584-590.
[2] KENNEDY J, EBERHART RC. Particle Swarm Optimization[J]. IEEE International Conference on Neural Networks,1995,4(8):1942-1948.
[3] KOTECHA J H, DJURIĆ P M. Gaussian Particle Filtering[J]. IEEE Transactions on Signal Processing, 2003, 51(10):2592-2601.
[4] ARULAMPALAM M S, MASKELL S, GORDON N, et al. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2):174-188.
[5] KROHLING R A. Gaussian Swarm: A Novel Particle Swarm Optimization Algorithm[C]//IEEE Conference on Cybernetics and Intelligent Systems.[S.l.]:IEEE,2004.
[6] WANG D, ZENG P, LI L, et al. A Novel Particle Swarm Optimization Algorithm[C]//IEEE International Conference on Software Engineering and Service Sciences.[S.l.]:IEEE,2010:408-411.
[7] THRUN S. Particle Filters in Robotics[J].Eighteenth Conference on Uncertainty in Artificial Intelligence,2002,19(2):511-518.
[8] DOUCET A, GORDON N, KRISHNAMURTHY V. Particle Filters for State Estimation of Jump Markov Linear Systems[J]. IEEE Transactions on Signal Processing,2001,49(3):613-624.
[9] DOUCET A. On Sequential Simulation-based Methods for Bayesian Filtering[D]. Cambridge:University of Cambridge, 1998.
[10] BAY K S, LEE S J, FLATHMAN D P, et al. Sequential Imputations and Bayesian Missing Data Problems[J]. Journal of the American Statistical Association, 1994, 89(425):278-288.
[11] 方正,佟国峰,徐心和.粒子群优化粒子滤波方法[J]. 控制与决策,2007,22(3): 273-277.
[12] 陈国良,张言哲,汪云甲,等. WiFi-PDR室内组合定位的无迹卡尔曼滤波算法[J]. 测绘学报, 2015, 44(12):1314-1321.
[13] 薛长虎,聂桂根,汪晶.扩展卡尔曼滤波与粒子滤波性能对比[J]. 测绘通报, 2016(4): 10-14.
[14] 邹韬,赵长胜,丁圳祥.有色观测噪声下的无迹卡尔曼滤波算法[J]. 测绘通报, 2015(6): 24-27.
[15] 王法胜,鲁明羽,赵清杰,等. 粒子滤波算法[J]. 计算机学报, 2014, 37(8):1679-1694.
[16] 任航. 基于拟蒙特卡洛滤波的改进式粒子滤波目标跟踪算法[J]. 电子测量与仪器学报, 2015(2):289-295.