Gradient-based particle filter algorithm for an ARX model with nonlinear communication output

Chen, Jing; Liu, Yanjun; Ding, Feng; Zhu, Quanmin

Abstract

A stochastic gradient (SG)-based particle filter (SG-PF) algorithm is developed for an ARX model with nonlinear communication output in this paper. This ARX model consists of two submodels, one is a linear ARX model and the other is a nonlinear output model. The process outputs (outputs of the linear submodel) transmitted over a communication channel are unmeasurable, while the communication outputs (outputs of the nonlinear submodel) are available, and both of the twotype outputs are contaminated by white noises. Based on the rich input data and the available communication output data, a SG-PF algorithm is proposed to estimate the unknown process outputs and parameters of the ARX model. Furthermore, a direct weight optimization method and the Epanechnikov kernel method are extended to modify the particle filter when the measurement noise is a Gaussian noise with unknown variance and the measurement noise distribution is unknown. The simulation results demonstrate that the SG-PF algorithm is effective.

Citation

Chen, J., Liu, Y., Ding, F., & Zhu, Q. (2018). Gradient-based particle filter algorithm for an ARX model with nonlinear communication output. IEEE Transactions on Systems Man and Cybernetics: Systems, https://doi.org/10.1109/TSMC.2018.2810277

• Journal Article Type: Article
• Acceptance Date: Jan 5, 2018
• Publication Date: Mar 7, 2018
• Deposit Date: Apr 12, 2018
• Publicly Available Date: Apr 12, 2018
• Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
• Print ISSN: 2168-2216
• Electronic ISSN: 2168-2232
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/TSMC.2018.2810277
• Keywords: ARX model, auxiliary model, parameter estimation, particle filter, stochastic gradient (SG)
• Public URL: https://uwe-repository.worktribe.com/output/870797
• Publisher URL: https://doi.org/10.1109/TSMC.2018.2810277
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