A Framework for Low Complexity Least-Squares Localization With High Accuracy
dc.contributor.author | Yan, J. | |
dc.contributor.author | Tiberius, C. | |
dc.contributor.author | Teunissen, Peter | |
dc.contributor.author | Bellusci, G. | |
dc.contributor.author | Janssen, G. | |
dc.date.accessioned | 2017-01-30T11:59:38Z | |
dc.date.available | 2017-01-30T11:59:38Z | |
dc.date.created | 2011-03-16T20:02:06Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Yan, Junlin and Tiberius, C.J.M. and Teunissen, Peter J.G. and Bellusci, Giovanni and Janssen, Gerard J.M. 2010. A Framework for Low Complexity Least-Squares Localization With High Accuracy. IEEE Transactions on Signal Processing. 58 (9): pp. 4836-4847. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/17099 | |
dc.identifier.doi | 10.1109/TSP.2010.2050313 | |
dc.description.abstract |
In this paper, a new framework is proposed for least-squares localization based on estimated ranges, coveringtime-difference-of-arrival (TDoA), time-of-arrival (ToA), and received signal strength (RSS) cases. The multidimensional nonlinear localization problem is first transformed to a lower dimension and then solved iteratively. Within the proposed transformed least-squares (TLS) framework, we introduce a method in which the localization problem is transformed to one dimension (1-D). In this way, compared to the classical nonlinear least-squares (NLS) type of methods, the amount of computations in each iteration is greatly reduced; a reduction of 67% for a 3-D positioning system is shown. Hence, the introduced 1-D iterative (1DI) method is fairly light on the computational load.The way to choose the 1-D parameter is proposed, and theoretical expressions for the convergence rate and the root- mean-squared error (RMSE) of the 1DI estimator are derived. Validation is performed mainly based on actual ultra-wideband (UWB) radio measurements, collected in typical office environments, with signal bandwidths varying from 0.5 to 7.5 GHz. Supplementary simulations are also included for validation. Results show that, in terms of RMSE, the 1DI method performs better than the linear least-squares (LLS) method, where the solution is obtained noniteratively, and performs similarly as NLS, especially in TDoA cases. | |
dc.publisher | IEEE Signal Processing Society | |
dc.subject | low complexity | |
dc.subject | ultrawideband (UWB) | |
dc.subject | Least-squares localization | |
dc.title | A Framework for Low Complexity Least-Squares Localization With High Accuracy | |
dc.type | Journal Article | |
dcterms.source.volume | 58 | |
dcterms.source.number | 9 | |
dcterms.source.startPage | 4836 | |
dcterms.source.endPage | 4847 | |
dcterms.source.issn | 1053-587X | |
dcterms.source.title | IEEE Transactions on Signal Processing | |
curtin.note |
Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
curtin.department | Department of Spatial Sciences | |
curtin.accessStatus | Open access |