In spite of major research initiatives by DARPA and other research organizations, precise indoor geolocation still remains as a challenge facing the research community. The core of this challenge is to understand the cause of large ranging errors in estimating the time of arrival (TOA) of the direct path between the transmitter and the receiver. Results of wideband measurement in variety of indoor areas reveal that large ranging errors are caused by severe multipath conditions and frequent occurrence of undetected direct path (UDP) situations. Empirical models for the behavior of the ranging error, which we refer to as the distance measurement error (DME), its relation to the distance between the transmitter and the receiver and the bandwidth of the system is needed for development of localization algorithms for precise indoor geolocation. The main objective of this dissertation is to design a direct empirical model for the behavior of the DME. In order to achieve this objective we provide a framework for modeling of DME, which relates the error to the distance between the transmitter and the receiver and bandwidth of the system. Using this framework we first designed a set of preliminary models for the behavior of the DME based on the CWINS proprietary measurement calibrated ray-tracing simulation tool. Then, we collected a database of 2934 UWB channel impulse response measurements at 3-8GHz in four different buildings to incorporate a variety of building materials and architectures. This database was used for the design of more in depth and realistic models for the behavior of the DME. The DME is divided into two components, Multipath-DME (MDME) and UDP-DME (UDME). Based on the empirical data, models for the behavior of each of these components are developed. These models reflect the sensitivity to bandwidth and show that by increasing the bandwidth MDME decreases. However in UDME the behavior is complicated. At first it reduces as we increase the bandwidth but after a certain bandwidth it starts to increase. In addition to these models through an analysis on direct path power versus the total power the average probability of having a UDP was calculated.

"A well-known challenge in estimating the distance of the antenna pair in time-of-arrival (ToA) based RF localization systems is the problem of obstruction of the direct path (DP) between transmitter and receiver. The absence of DP component in received channel profile creates undetected direct path (UDP) conditions. UDP condition, in turn, will cause occurrence of unexpected large ranging errors which pose serious challenge to precise indoor localization. Analysis of the behavior of the ranging error in such conditions is essential for the design of precise ToA-based indoor localization systems. This dissertation discusses two open problems in ToA-based indoor localization systems. The first contribution of this dissertation discusses the problem of modeling of dynamic behavior of ranging error. We propose a novel analytical framework for analysis of dynamic spatial variations of ranging error observed by a mobile user based on an application of Markov chain. The model relegates the behavior of ranging error into four main categories associated with four states of Markov process. Parameters of distributions of ranging error in each Markov state are extracted from empirical data collected from a measurement-calibrated ray tracing algorithm simulating a typical office environment. The analytical derivation of parameters of the Markov model employs the existing path-loss models for first detected path and total multipath received power in the same office environment. Results of simulated errors from the Markov model and actual errors from empirical data show close agreement. The second contribution of this dissertation discusses the problem of identification of UDP condition given an unknown channel profile. Existing of UDP condition in a channel profile poses serious degradation to ranging estimate process. Therefore, identification of occurrence of UDP condition is of our subsequent concern. After identification, the second step is to mitigate ranging errors in such conditions. In this dissertation we present two methodologies, based on binary hypothesis testing and an application of artificial neural network design, to identify UDP conditions and mitigate ranging error using statistics extracted from wideband frequency-domain indoor measurements conducted in typical office building. "