Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Making Driver Modeling Attractive
IEEE Intelligent Systems
Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains
Theoretical Computer Science
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Optimum tracking with evolution strategies
Evolutionary Computation
Self-adaptation of evolution strategies under noisy fitness evaluations
Genetic Programming and Evolvable Machines
Nonlinear filtering for speaker tracking in noisy and reverberant environments
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Generating fuzzy rules for target tracking using a steady-stategenetic algorithm
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Hi-index | 0.00 |
This paper describes a new method for the online parameter optimization of various models used to represent the target dynamics in particle filters. The optimization is performed with an evolutionary strategy algorithm, by using the performance of the particle filter as a basis for the objective function. Two different approaches to forming the objective function are presented: the first assumes knowledge of the true source position during the optimization, and the second uses the position estimates from the particle filter to form an estimate of the current ground-truth data. The new algorithm has low computational complexity and is suitable for real-time implementation. A simple and intuitive real-world application of acoustic source localization and tracking is used to highlight the performance of the algorithm. Results show that the algorithm converges to an optimum tracker for any type of dynamics model that is capable of representing the target dynamics.