Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145323
Title: Bayesian filtering with unknown sensor measurement losses
Authors: Zhang, Jiaqi
You, Keyou
Xie, Lihua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Zhang, J., You, K., & Xie, L. (2019). Bayesian Filtering With Unknown Sensor Measurement Losses. IEEE Transactions on Control of Network Systems, 6(1), 163–175. doi:10.1109/tcns.2018.2802872
Project: RG78/15
Journal: IEEE Transactions on Control of Network Systems
Abstract: This paper studies the state estimation problem of a stochastic nonlinear system with unknown sensor measurement losses. If the estimator knows the sensor measurement losses of a linear Gaussian system, the minimum variance estimate is easily computed by the celebrated intermittent Kalman filter (IKF). However, this will no longer be the case when the measurement losses are unknown and/or the system is nonlinear or non-Gaussian. By exploiting the binary property of the measurement loss process and the IKF, we design three suboptimal filters for the state estimation, that is, BKF-I, BKF-II, and RBPF. The BKF-I is based on the MAP estimator of the measurement loss process and the BKF-II is derived by estimating the conditional loss probability. The RBPF is a particle filter-based algorithm that marginalizes out the loss process to increase the efficiency of particles. All of the proposed filters can be easily implemented in recursive forms. Finally, a linear system, a target tracking system, and a quadrotor's path control problem are included to illustrate their effectiveness, and show the tradeoff between computational complexity and estimation accuracy of the proposed filters.
URI: https://hdl.handle.net/10356/145323
ISSN: 2325-5870
DOI: 10.1109/TCNS.2018.2802872
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCNS.2018.2802872.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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