Robust regression and outlier detection
Robust regression and outlier detection
Robust estimator for non-line-of-sight error mitigation in indoor localization
EURASIP Journal on Applied Signal Processing
Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
Indoor geolocation in the absence of direct path
IEEE Wireless Communications
Nonline-of-sight error mitigation in mobile location
IEEE Transactions on Wireless Communications
Analysis of wireless geolocation in a non-line-of-sight environment
IEEE Transactions on Wireless Communications
Indoor geolocation science and technology
IEEE Communications Magazine
Ranging in a dense multipath environment using an UWB radio link
IEEE Journal on Selected Areas in Communications
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This paper deals with a wireless indoor positioning problem in which the location of a tag is estimated from range measurements taken by fixed beacons. The measurements may be affected by non-line-of-sight (NLOS) errors that must be mitigated. We discuss a maximum likelihood (ML) positioning technique that assumes a realistic model for the range errors and a signature database providing information on the propagation conditions at every hypothesized tag spot. The database can be gathered from knowledge of the service area infrastructure and through pre-measurements. It is given in the form of a map indicating, at any node of a close-mesh grid, the nature (LOS/NLOS) of the link between that node and each beacon. The performance of the positioning algorithm is assessed by simulation and is compared with other methods available in literature. The results show that the proposed technique provides significant improvements and is robust against mismatches between true and assumed values of the parameters in the range error model. Comparisons with the Cramer-Rao Bound are made.