Indoor Tracking With the Generalized t-Distribution Noise Model
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2017
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| Online Access: | https://hdl.handle.net/10356/82832 http://hdl.handle.net/10220/42322 |
| _version_ | 1824454772541882368 |
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| author | Yin, Le Liu, Shuo Ho, Weng Khuen Ling, Keck Voon |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yin, Le Liu, Shuo Ho, Weng Khuen Ling, Keck Voon |
| author_sort | Yin, Le |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:27:38Z |
| format | Journal Article |
| id | ntu-10356/82832 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:27:38Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/828322020-03-07T13:57:23Z Indoor Tracking With the Generalized t-Distribution Noise Model Yin, Le Liu, Shuo Ho, Weng Khuen Ling, Keck Voon School of Electrical and Electronic Engineering Indoor positioning and tracking Robust estimation 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. Accepted version 2017-05-03T09:23:53Z 2019-12-06T15:06:31Z 2017-05-03T09:23:53Z 2019-12-06T15:06:31Z 2017 Journal Article 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. 1063-6536 https://hdl.handle.net/10356/82832 http://hdl.handle.net/10220/42322 10.1109/TCST.2017.2692737 en IEEE Transactions on Control Systems Technology © 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]. 12 p. application/pdf |
| spellingShingle | Indoor positioning and tracking Robust estimation Yin, Le Liu, Shuo Ho, Weng Khuen Ling, Keck Voon Indoor Tracking With the Generalized t-Distribution Noise Model |
| title | Indoor Tracking With the Generalized t-Distribution Noise Model |
| title_full | Indoor Tracking With the Generalized t-Distribution Noise Model |
| title_fullStr | Indoor Tracking With the Generalized t-Distribution Noise Model |
| title_full_unstemmed | Indoor Tracking With the Generalized t-Distribution Noise Model |
| title_short | Indoor Tracking With the Generalized t-Distribution Noise Model |
| title_sort | indoor tracking with the generalized t distribution noise model |
| topic | Indoor positioning and tracking Robust estimation |
| url | https://hdl.handle.net/10356/82832 http://hdl.handle.net/10220/42322 |
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