Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
A genetic feature weighting scheme for pattern recognition
Integrated Computer-Aided Engineering
Information granulation as a basis of fuzzy modeling
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A soft computing approach to localization in wireless sensor networks
Expert Systems with Applications: An International Journal
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
An ant stochastic decision based particle filter and its convergence
Signal Processing
Computers & Mathematics with Applications
Particle filter with multimode sampling strategy
Signal Processing
Ant Colony Estimator: An intelligent particle filter based on ACOR
Engineering Applications of Artificial Intelligence
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Particle filters perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. Unfortunately, there are some cases in which most particles are concentrated prematurely at a wrong point, thereby losing diversity and causing the estimation to fail. In this paper, genetic algorithms (GAs) are incorporated into a particle filter to overcome this drawback of the filter. By using genetic operators, the premature convergence of the particles is avoided and the search region of particles enlarged. The GA-inspired proposal distribution is proposed and the corresponding importance weight is derived to approximate the given target distribution. Finally, a computer simulation is performed to show the effectiveness of the proposed method.