A framework for low complexity least-squares localization with high accuracy

Authors:
Junlin Yan;Christian C. J. M. Tiberius;Peter J. G. Teunissen;Giovanni Bellusci;Gerard J. M. Janssen
Affiliations:
Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands;Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands;Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands and Department of Spatial Sciences, Curtin University of Technology, Perth, Australia;Faculty of EEMCS, Delft University of Technology, CD, Delft, The Netherlands;Faculty of EEMCS, Delft University of Technology, CD, Delft, The Netherlands
Venue:
IEEE Transactions on Signal Processing
Year:
2010

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Abstract

In this paper, a new framework is proposed for least-squares localization based on estimated ranges, covering time-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.