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Joint Parameter and State Estimation Based on Marginal Particle Filter and Particle Swarm Optimization

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Abstract

In this paper, a method for the dual estimation is proposed. This approach combines extended marginal particle filter (EMPF) with particle swarm optimization (PSO) for simultaneous estimation of state and parameter values in nonlinear stochastic state–space models. In the proposed method, the states are estimated by EMPF and the parameters are estimated by PSO. The performance of proposed algorithm is evaluated in two examples. Simulation results demonstrate the feasibility and efficiency of the proposed method.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

    Ramazan Havangi

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  1. Ramazan Havangi
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Correspondence to Ramazan Havangi.

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Havangi, R. Joint Parameter and State Estimation Based on Marginal Particle Filter and Particle Swarm Optimization. Circuits Syst Signal Process 37, 3558–3575 (2018). https://doi.org/10.1007/s00034-017-0721-4

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  • DOI: https://doi.org/10.1007/s00034-017-0721-4

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