Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82832
Title: Indoor Tracking With the Generalized t-Distribution Noise Model
Authors: Yin, Le
Liu, Shuo
Ho, Weng Khuen
Ling, Keck Voon
Keywords: Indoor positioning and tracking
Robust estimation
Issue Date: 2017
Source: Yin, L., Liu, S., Ho, W. K., & Ling, K. V. (2017). Indoor Tracking With the Generalized t-Distribution Noise Model. IEEE Transactions on Control Systems Technology, 99, 1-12.
Series/Report no.: IEEE Transactions on Control Systems Technology
Abstract: An indoor tracking system with forgetting factor and generalized t-distribution (GT) noise model is proposed in this paper. It consists of first using the weighted centroid formulas to give an estimate of the position and then a filter with GT noise model to improve on the estimate. A common problem with indoor tracking is the noisy disturbances and this paper uses the GT to model them. By being a superset encompassing Gaussian, uniform, t, Cauchy, and double exponential distributions, GT has the flexibility to characterize noise with Gaussian or non-Gaussian statistical properties. Because of the more accurate noise model, the filter with GT noise model can produce a better estimate than that of the Kalman filter which makes the usual assumption of Gaussian noise. An equation to compute the variance of the estimation error is also derived in this paper. For verification, 200 tracking experiments were conducted. The variance obtained from the experiments matched the variance calculated from the equation. The variance of the estimation error from the filter with GT noise model is smaller than that of the Kalman filter. Another experiment at the lift landing showed that the proposed filter with GT noise model is also less affected by outliers.
URI: https://hdl.handle.net/10356/82832
http://hdl.handle.net/10220/42322
ISSN: 1063-6536
DOI: 10.1109/TCST.2017.2692737
Rights: © 2017 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: [http://dx.doi.org/10.1109/TCST.2017.2692737].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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