Abstract
In this chapter, a new class of filters based on swarm intelligence is introduced for nonlinear systems state estimation. As a subset of heuristic filters, swarm filters formulate a nonlinear system state estimation problem as a stochastic dynamic optimization problem and utilize swarm intelligence techniques such as particle swarm optimization and ant colony optimization to find and track the best estimate. As a subset of nonlinear filters, swarm filters can successfully compete with well-known nonlinear filters such as unscented Kalman filter, etc.
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Notes
- 1.
CACF code is available at http://ae.sharif.ir/Faculty-Resume/Nobahari.php.
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Nobahari, H., Sharifi, A., MohammadKarimi, H. (2013). Swarm Intelligence Techniques Applied to Nonlinear Systems State Estimation. In: Chatterjee, A., Nobahari, H., Siarry, P. (eds) Advances in Heuristic Signal Processing and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37880-5_10
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DOI: https://doi.org/10.1007/978-3-642-37880-5_10
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