Evolutionary Optimization of Dynamics Models in Sequential Monte Carlo Target Tracking

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 fun...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2009-08, Vol.13 (4), p.879-894
Main Authors: Johansson, A.M., Lehmann, E.A.
Format: Article
Language:English
Subjects:
Acoustical measurements and instrumentation
Acoustics
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Artificial intelligence
Australia
Bayesian methods
Computer science
control theory
systems
Computer simulation
Covariance matrix
Covariance matrix adaptation
dual estimation
dynamics model
Estimates
Evolutionary
evolutionary strategy
Exact sciences and technology
Filtering algorithms
Fundamental areas of phenomenology (including applications)
Global Positioning System
Iterative algorithms
Learning and adaptive systems
Mathematical analysis
Mathematical models
Monte Carlo methods
Optimization
particle filter
Particle filters
Physics
State estimation
Target tracking
Theoretical computing
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Summary: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2009.2017518