"Precise Tracking of Things (PToT) using RF signals has posed a serious challenge in an indoor environment. The precision localization information is an enabler for better coordinated-tasks and is essential for a successful launch of many emerging applications. PToT relies on two principal components, a novel navigation (tracking) algorithm, and a hybrid 3D fingerprint database. In this dissertation, we begin by using the two widely known ranging techniques, Time Of Arrival (TOA) associated with Ultra-wideband (UWB) and Received Signal Strength (RSS) with WiFi signals. First, we use the theoretical models derived from empirical measurement of TOA and RSS to analyze the performance of hybrid (WiFi & UWB) cooperative localization accuracy in a multi-robot operation in a typical office environment. To measure the performance of this hybrid localization, we derive a mathematical formulation for the Crame ́r-Rao-Lower- Bound (CRLB). The hybrid method shows more accuracy over WiFi-only approach. In achieving more precision, we extend our work. Second, we introduce a novel approach, a Kernel Method Particle Filter (KMPF) for tracking and predicting the position by accessing the information created by hybrid 3D fingerprint database. We derive the mathematical and statistical framework for the Particle Filter based on the statistical assumptions about the behavior of channel models. We also describe the formation of one of the necessary PToT component, a 3D fingerprint database. We compare the performance of the KMPF against the CRLB using WiFi signal channel models."

"The time-of-arrival (ToA)-based localization technique performs superior in line-of-sight (LoS) conditions, and its accuracy degrades drastically in proximity of micro-metals and human body, when LoS conditions are not met. This calls for modeling and formulation of Direct Path (DP) to help with mitigation of ranging error. However, the current propagation tools and models are mainly designed for telecommunication applications via focus on delay spread of wireless channel profile, whereas ToA-based localization strive for modeling of DP component. This thesis provides a mitigation to the limitation of existing propagation tools and models to computationally capture the effects of micro-metals and human body on ToA-based indoor localization. Solutions for each computational technique are validated by empirical measurements using Ultra-Wide-Band (UWB) signals. Finite- Difference-Time-Domain (FDTD) numerical method is used to estimate the ranging errors, and a combination of Uniform-Theory-of-Diffraction (UTD) ray theory and geometrical ray optics properties are utilized to model the path-loss and the ToA of the DP obstructed by micro- metals. Analytical UTD ray theory and geometrical ray optics properties are exploited to model the path-loss and the ToA of the first path obstructed by the human body for the scattering scenarios. The proposed scattering solution expanded to analytically model the path-loss and ToA of the DP obstructed by human body in angular motion for the radiation scenarios."

For the mass demands of wireless communication services, the mobile location technologies have drawn much attention of the governments, academia, and industries around the world. In wireless communication, one of the main problems with accurate location is nonline of sight (NLoS) propagation. To solve the problem, we present a new location algorithm with clustering technology by utilizing the geometrical feature of cell layout, time of arrival (ToA) range measurements, and three base stations. The mobile location is estimated by solving the optimal solution of the objective function based on the high density cluster. Simulations study was conducted to evaluate the performance of the algorithm for different NLoS error distributions and various upper bound of NLoS error. The results of our experiments demonstrate that the proposed algorithm is significantly more effective in location accuracy than range scaling algorithm, linear lines of position algorithm, and Taylor series algorithm, and also satisfies the location accuracy demand of E-911.

The main purpose of this thesis is to estimate the signal arrival time in low rate wireless personal area network systems. In a dense multipath environment, the generalized maximum-likelihood (GML) algorithm can be used for the time-of-arrival (TOA) estimation. Nevertheless, the GML algorithm is very time-consuming and usually takes a long period of time, and sometimes fails to converge. Hence, a simplified scheme that would improve the algorithm is investigated. In the simplified scheme, the search is executed in a sequential form. Two threshold parameters are determined for the stop condition in the algorithm. One threshold is on the arrival time of estimated path, while the other is on the fading amplitude of estimated path. The determination of thresholds can be based on the minimum error probability, which is defined as the sum of the false alarm probability and the missing probability. Root-mean-square error statistics are used to improve the thresholds setting. In this scheme, candidate pairs of thresholds are evaluated in each appropriate range. To solve the problem that the root-mean-square error value for each pair of thresholds is calculated, the simulated annealing is adopted for searching the best threshold pair. The problem that all possible solutions in a large range must be evaluated can be solved by simulated annealing. From the simulation results, it is seen that, while the signal-to-noise ratio is larger or equal to 4dB, the proposed scheme can achieve better performance than the root-mean-square error statistics scheme.

In wireless location systems, received signals may be influenced by non-line of sight (NLOS) propagation errors, which yield severe degradation of location accuracy.Therefore, to distinguish how many measurement signals are line-of-sight (LOS) and to identify them simultaneously will contribute to the increase of location accuracy.We propose a method based on recursive hypothesis testing algorithm, and use residual information to determine whether the NLOS errors are present in measurements. Since the probability distribution of measurements with NLOS errors is different from that of measurements without NLOS errors, a likelihood ratio test can be used in determining the LOS/NLOS status of the measurements. To search for an optimal threshold for the hypothesis testing, particle filtering optimization(PFO) is adopted. The PFO algorithm uses particle filtering to find the best threshold for determining the status of signals measured at all base stations (BSs). In the PFO algorithm, the clustering property of K-means is also used in separating particles, thereby the search of optimal threshold may be implemented in parallel.In this thesis, we focus on the hybrid TOA/AOA (time of arrical/angle of arrival) location method, in which localization only uses the LOS location measurements to calculate the location of a mobile station. Simulation results show that the proposed algorithm performs better than other algorithms which suffer from different degrees of NLOS errors. The proposed scheme also obtains higher identification rate of LOS-BSs in different situations by using the optimal thresholds for status detection.

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Generally, a ground telemetry station for launch vehicle (LV) has tracking function only; therefore, position measurements depend on radar. Time of arrival (TOA) and angle of arrival (AOA) are typical location techniques for emitting targets. In this paper, we propose a Combined TOA and AOA localization method for LV using two ground stations. When transmitter (Tx) time is not known, it is necessary to make virtual onboard timer for TOA estimation. The virtual onboard timer generates time stamps of streaming frame according to data rate. First station which is located in space center has no tracking function. But it can generate the virtual onboard timer. Second station has tracking function, so it generates AOA information. By solving sphere equation(s) of TOA from at least one station and a line equation of AOA, target position in three-dimensions (3D) can be obtained. We confirm the localization performance by means of comparison with an on-board GPS of a real launch mission.

Passive localization of aircraft in flight using signal time of arrival (TOA) poses some unique challenges. The sensors must be deployed in an approximately coplanar configuration, which produces significant vertical uncertainty in the estimated position. This dissertation examines the traditional algorithms used in passive localization. It presents general forms of linear TOA, time difference of arrival (TDOA), angle of arrival (AOA), and frequency difference of arrival (FDOA) equations from the literature and explains how to apply an intuitive geometric interpretation of these equations. It presents two novel algorithms for passive localization. One uses a one dimensional AOA (1AOA) to improve the vertical estimate. The other employs an a priori estimate to approximate the non-linear localization problem as a linear problem and produce a high quality position estimate. A comprehensive survey of the literature is presented. This dissertation provides a summary and classification of passive localization algorithms from the literature with simple descriptions of how the form of the equations relate to their numerical stability. It presents two novel algorithms for passive localization. The hybrid multilateration and triangulation algorithm improves wide area multilateration by using vertical 1AOA to constrain the vertical position. The multilateration with a priori estimates algorithm provides a linear localization method that utilizes previous location estimates.

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