Distributed Recursive Gaussian Processes for RSS Map Applied to Target Tracking
We propose a distributed recursive Gaussian process (drGP) regression framework for building received-signal-strength (RSS) map. The proposed framework adopts independent mobile devices in prescribed local areas to construct local RSS maps through recursive computation of the posterior distribution...
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| Vydáno v: | IEEE journal of selected topics in signal processing Ročník 11; číslo 3; s. 492 - 503 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.04.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1932-4553, 1941-0484 |
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| Abstract | We propose a distributed recursive Gaussian process (drGP) regression framework for building received-signal-strength (RSS) map. The proposed framework adopts independent mobile devices in prescribed local areas to construct local RSS maps through recursive computation of the posterior distribution of the RSS on a fixed set of grids as training data gradually become available. The training input positions can be either precise or subject to errors of known distribution. All the local RSS maps are then fused to give a global map in the second step. The proposed framework is of significantly reduced computational complexity and scalable to big data generated from large-scale sensor networks. We further demonstrate its use in both static fingerprinting and mobile target tracking. The experimental results show that with our distributed framework satisfactory positioning accuracy can be achieved with much less complexity and storage than the standard framework. |
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| AbstractList | We propose a distributed recursive Gaussian process (drGP) regression framework for building received-signal-strength (RSS) map. The proposed framework adopts independent mobile devices in prescribed local areas to construct local RSS maps through recursive computation of the posterior distribution of the RSS on a fixed set of grids as training data gradually become available. The training input positions can be either precise or subject to errors of known distribution. All the local RSS maps are then fused to give a global map in the second step. The proposed framework is of significantly reduced computational complexity and scalable to big data generated from large-scale sensor networks. We further demonstrate its use in both static fingerprinting and mobile target tracking. The experimental results show that with our distributed framework satisfactory positioning accuracy can be achieved with much less complexity and storage than the standard framework. |
| Author | Feng Yin Gunnarsson, Fredrik |
| Author_xml | – sequence: 1 surname: Feng Yin fullname: Feng Yin email: yinfeng@cuhk.edu.cn organization: Shenzhen & Shenzhen Res. Inst. of Big Data, Univ. of Hong Kong, Shenzhen, China – sequence: 2 givenname: Fredrik surname: Gunnarsson fullname: Gunnarsson, Fredrik email: fredrik.gunnarsson@ericsson.com organization: Ericsson Res., Linkoping, Sweden |
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| Cites_doi | 10.1109/ICASSP.2013.6638281 10.1145/1317425.1317431 10.1109/WCNC.2005.1424905 10.1017/CBO9781139344203 10.3390/s150922587 10.1016/j.patrec.2014.03.004 10.1109/78.978396 10.1109/IROS.2008.4651188 10.1109/TSP.2003.814623 10.1109/MSP.2014.2332611 10.1162/089976600300014908 10.15607/RSS.2006.II.039 10.1109/MSP.2013.2246292 |
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| SubjectTerms | Approximation algorithms Big Data Complexity Distributed algorithm Fingerprinting Gaussian process Gaussian processes Kernel Mobile communication particle filtering recursive algorithm Recursive functions RSS fingerprinting Signal processing algorithms Tracking Training |
| Title | Distributed Recursive Gaussian Processes for RSS Map Applied to Target Tracking |
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