With the rapid development of wireless networking technology and the great growth of the service demand, accurate location estimation of a mobile station (MS) in an indoor wireless system has gained considerable attention. Since most wireless communication systems used for indoor position location may suffer from dense NLOS propagation error, which leads to a severe degradation of position accuracy. In this thesis, we propose a hybrid TOA/RSSI wireless positioning technique for indoor UWB systems to gain favorable position accuracy, by using the fine resolution of UWB signals, geometrical feature of cell layout, the path loss model and the received signal strength, based on the time of arrival (TOA) range measurements. The algorithm induces the objective function from the geometrical relationships of the base stations (BSs) and TOA range circles, and utilizes the received signal strength and pre-estimated path loss model, which should be well approximating the propagation conditions, to discriminate between LOS or NLOS range measurements and define the weight factors used to describe how credible TOA range measurements are and take effect on the objective function during the location calculating process. Simulation results show that the positioning accuracy of the hybrid TOA/RSSI method is much higher than that of other positioning methods under different distributed NLOS errors.

This thesis focuses on the least-squares formulation of the non-collaborative and collabo- rative position location problems. For the non-collaborative problem, characterization encompassing the number of minima and the causes thereof is provided. Based on these efforts, we propose an improvement to the existing modified parallel projection method (MPPM), the reflected parallel projection method (RPPM). We show that the global minimum to the non-collaborative objective function can nearly always be found using the non-optimal reflected parallel projection method (RPPM). For the collaborative position location problem, we provide a characterization that shows a heavy tail of root-mean-square (RMS) error due to a small percentage of simulated node/anchor layouts when solved by the iterative parallel projection method (IPPM). We provide an identification technique that successfully identifies most layouts that show large RMS error followed by a proposed solution to improve the accuracy in those problematic layouts. Finally, we report the findings of a measurement campaign that validates our Gaussian model for line-of-sight (LOS) noise and shows that, for these particular measurements, non-line-of-sight (NLOS) noise is difficult to accurately model and can be very large. This research was supported by a Bradley Fellowship from Virginia Tech's Bradley Department of Electrical and Computer Engineering, made possible by an endowment from the Harry Lynde Bradley Foundation. / Master of Science