A Novel Approach to Overcome Sample Impoverishment Problem of Particle Filter using Chaotic Crow Search Algorithm
Abstract
Generic Particle Filter is extensively used in the area of computer vision for non-Linear and non-Gaussian state estimation. However, Generic Particle Filter suffers from the problem of sample impoverishment and particle degeneracy. Aim of the research paper is to propose a method, using Chaotic Crow Search Algorithm as resampling method to overcome these problems of generic particle filter. The proposed method has been simulated on benchmark 1-D and 2-D state estimation problems. Simulation results of the proposed method are compared with Generic Particle Filter, Particle Filter- Particle Swarm Optimization and Particle Filter-Backtracking Search Optimization. On average of the outcome, we have achieved RMSE value of 2.0214 for 1-D problem and value of 0.0281 for 2-D problem for the proposed method. Results demonstrate that our method not only outperforms other methods but also achieve high accuracy with minimum computational requirement.
How to cite this article:
Kumar A, Sharma K, Singh G. A Novel Approach to Overcome Sample Impoverishment Problem of Particle Filter using Chaotic Crow Search Algorithm. J Engr Desg Anal 2021; 4(1): 1-7.
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