Conference Paper

Evolutionary particle filter: Re-sampling from the genetic algorithm perspective

ARC Centre of Excellence for Autonomous Syst., Univ. of Technol., Sydney, NSW, Australia
DOI: 10.1109/IROS.2005.1545119 Conference: Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on
Source: IEEE Xplore


The sample impoverishment problem in particle filters is investigated from the perspective of genetic algorithms. The contribution of this paper is in the proposal of a hybrid technique to mitigate sample impoverishment such that the number of particles required and hence the computation complexities are reduced. Studies are conducted through the use of Chebyshev inequality for the number of particles required. The relationship between the number of particles and the time for impoverishment is examined by considering the takeover phenomena as found in genetic algorithms. It is revealed that the sample impoverishment problem is caused by the resampling scheme in implementing the particle filter with a finite number of particles. The use of uniform or roulette-wheel sampling also contributes to the problem. Crossover operators from genetic algorithms are adopted to tackle the finite particle problem by re-defining or re-supplying impoverished particles during filter iterations. Effectiveness of the proposed approach is demonstrated by simulations for a monobot simultaneous localization and mapping application.

Download full-text


Available from: Gu Fang,
  • Source
    • "; incorporation of genetic algorithms into a particle filter [5] [6]; and many others [7] [8] [9]. All these strategies , although extremely interesting and suitable for the orbit determination problem, are not in the scope of this work. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The paper aims at discussing techniques for administering one implementation issue that often arises in the application of particle filters: sample impoverishment. Dealing with such problem can significantly improve the performance of particle filters and can make the difference between success and failure. Sample impoverishment occurs because of the reduction in the number of truly distinct sample values. A simple solution can be to increase the number of particles, which can quickly lead to unreasonable computational demands, which only delays the inevitable sample impoverishment. There are more intelligent ways of dealing with this problem, such as roughening and prior editing, procedures to be discussed herein. The nonlinear particle filter is based on the bootstrap filter for implementing recursive Bayesian filters. The application consists of determining the orbit of an artificial satellite using real data from the GPS receivers. The standard differential equations describing the orbital motion and the GPS measurements equations are adapted for the nonlinear particle filter, so that the bootstrap algorithm is also used for estimating the orbital state. The evaluation will be done through convergence speed and computational implementation complexity, comparing the bootstrap algorithm results obtained for each technique that deals with sample impoverishment.
    Mathematical Problems in Engineering 01/2015; 2015:1-9. DOI:10.1155/2015/168045 · 0.76 Impact Factor
  • Source
    • "Table II. Categories of primary PDO approaches to review PDO tool Typical PDO property Primary references Kernel smoothing Blind [41] [42] [43] [44] [45] [48] Data-driven method MCMC Sighted [16] [25] [33] [37] [40] [50] [51] [52] Mean-shift Sighted [33] [53] [54] [55] [56] AI algorithms (Evolution and population) GA Blind [57] [58] [59] [60] [71] PSO Sighted [63] [64] [65] [66] [91] ACO Sighted [67] [68] [69] [70] ML techniques Clustering Blind [53] [77] [78] [79] [80] [81] [82] [83] Merging/splitting Blind [36] [39] [76] Others scatter search process [61] [72], support vector machines & support vector data description [29] "
    [Show abstract] [Hide abstract]
    ABSTRACT: During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degeneracy and impoverishment. We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices. These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization), machine learning approaches (e.g., clustering, splitting and merging) and their hybrids, forming a coherent standpoint to enhance the particle filter. The working mechanism, interrelationship, pros and cons of these approaches are provided. In addition, approaches that are effective for dealing with high-dimensionality are reviewed. While improving the filter performance in terms of accuracy, robustness and convergence, it is noted that advanced techniques employed in PF often causes additional computational requirement that will in turn sacrifice improvement obtained in real life filtering. This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters.
    Expert Systems with Applications 06/2014; 41(8):3944–3954. DOI:10.1016/j.eswa.2013.12.031 · 2.24 Impact Factor
  • Source
    • "This combination outperforms other existing filters but it comes at the cost of heavy computational burden. Recent approaches are based on optimization methods to avoid the resampling stage by biasing the prior sample towards regions of the state space with high likelihood [11] [15] [17]. An approach that employs Particle Swarm Optimization (PSO) for this purpose was recently proposed [14]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: ABSTRACT We propose a Particle Filter model that incorporates Parti- cle Swarm Optimization for predicting systems with multi- plicative noise. The proposed model employs a conventional multiobjective optimization approach to weight the likeli- hood and prior of the filter in order to alleviate the particle
    Genetic and Evolutionary Computation Conference, GECCO 2008, Proceedings, Atlanta, GA, USA, July 12-16, 2008; 01/2008
Show more