The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Mobile Fading Channels
Error characteristics and calibration-free techniques for wireless LAN-based location estimation
Proceedings of the second international workshop on Mobility management & wireless access protocols
The Horus WLAN location determination system
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Extended Kalman filter channel estimation for line-of-sight detection in WCDMA mobile positioning
EURASIP Journal on Applied Signal Processing
Spatial Diversity in Signal Strength based WLAN Location Determination Systems
LCN '07 Proceedings of the 32nd IEEE Conference on Local Computer Networks
Indoor geolocation science and technology
IEEE Communications Magazine
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The fundamental cause of localization error in an indoor environment is fading and spreading of the radio signals due to scattering, diffraction, and reflection. These effects are predominant in regions where there is no-line-of-sight (NLoS) between the transmitter and the receiver. Efficient algorithms are needed to identify the subset of receivers that provide better localization accuracy. This paper introduces a new parameter called the R-Factor to indicate the extent of radial distance estimation error introduced by a receiver and to select a subset of receivers that result in better accuracy in real-time location determination systems (RTLS). In addition, it was demonstrated that location accuracy improves with R-factor reduction. Further, it was shown that for a given R-factor threshold, localization accuracy is enhanced either by increasing the number of receivers that fall below this threshold or by increasing the diversity channel count with appropriate combining of signals from diversity. Therefore, existing localization algorithms can utilize R-factor and diversity through selection combining to improve accuracy. Both analytical and experimental results are included to justify the theoretical results in terms of improvement in accuracy by using R-factor.