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.
CITATION STYLE
Nobahari, H., Sharifi, A., & Mohammadkarimi, H. (2013). Swarm intelligence techniques applied to nonlinear systems state estimation. In Advances in Heuristic Signal Processing and Applications (Vol. 9783642378805, pp. 219–241). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-37880-5_10
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