Localization is one of the most interesting research areas in wireless networks. It is mostly used for tracking and monitoring applications such as traffic monitoring, search and rescue, navigation and so on. A good quality system can be defined from its accuracy when operating in severe interference environments that contaminate the signals and therefore reduce the system performance. The main issue for localization is channel propagation, e.g., line of sight or non-line of sight channel which should be studied in order to improve the system efficiency.    In order to perform a localization, most algorithms use two steps: ranging and positioning. For ranging, the two popular techniques that are widely used for distance measurement are received signal strength (RSS) and time of arrival (TOA). RSS ranging technique uses the power of the received signals to identify the distance between a transmitter and a receiver. TOA ranging technique uses time of the signal traveling between a transmitter and a receiver to identify the distance, thus it requires synchronization. The measurements are processed by using a localization algorithm afterwards. However, these techniques suffer from multipath fading and other errors, so there always exists error in the estimated position.    In this thesis, TOA ranging technique is used for different estimation methods. Simulation results are performed using MATLAB, while the real results are obtained from Pozyx indoor positioning platform. Several estimation algorithms comprising of maximum likelihood (ML), linearized least square (LLS), weighted centroid (WC), and fingerprinting (FP) are studied in detail. The testing area is indoor environment which is suitable for LOS, NLOS and combined situations. The measured data is then used for ranging and localization. We concentrate on comparing and discussing these results in this thesis.

We consider the problem of localizing objects in weakly labeled images/videos. An image/video (e.g., Flickr image and YouTube video) is weakly labeled if it is associated with a tag describing the main object present in the image/video. It is weakly labeled because the tag only indicates the presence/absence of the object, but does not provide the detailed spatial location of the object. Given an image/video with an object tag, our goal is to localize the object in it. In this thesis, we propose two novel techniques to handle this challenging problem. First, we build a video-specific object appearance model and then incorporate temporal consistency information to localize the object. Second, we make use of existing detectors of some other object classes (which we call "familiar objects") to build the appearance model of the unseen object class (i.e., the object of interest). Experimental results show the effectiveness of the proposed methods. / October 2016

In modern life, there are many applications where positioning plays an important role. People have developed the global positioning system (GPS) to locate world wide position with error in decameter scales, which brings people much convenience. However, the accuracy of GPS is too low for indoor localization. The signals will drop down due to the signal attenuation caused by construction materials. With the well-developed GPS being indispensable for outdoor activities, many researchers have been also devoted to seeking an indoor positioning system to realize indoor localization with acceptable error. Indoor localization can be very useful in different situations, like locating, tracking, navigation and identification. For example, in the mall, locating the exact goods for customers can provide much convenience and benefits. Locating and tracking in the airport can greatly help passengers save their time and energy in reaching the destination. In another general scenario of identification, the population of observed targets is usually larger than just one. Hence, only with small error, indoor localization system (ILS) can be able to identify the targets despite the neighbors. Due to the emerging and urging demands of increasing the accuracy of indoor localization, we propose a novel design of three dimensional (3-D). optical receiver for visible light communication (VLC) indoor positioning system. First, we model the optical wireless channel. Then we utilize modified triangulation method to obtain more robust receiver position by using at least two light-emitting diodes (LEDs) and one receiver consisting of nine photodetectors (PDs). Finally, the improved algorithm is implemented and the results are shown under our three dimensional multiple photodetectors (multi-PDs) structure receiver. In the simulation, we take the parameters of Lambertian radiation pattern, LEDs and PDs as those shown in [1] . To be noticed, our design of multi-PDs receiver is fully expanded into three dimensions compared with the pyramid receiver (PR), which allows indoor positioning with our receiver structure to be more robust to the higher or corner positions. The details will be explained in the following sections. Based on Multiple-Photodiodebased Indoor Positioning algorithm [1], the indoor positioning algorithm is improved by redefining the optimization problem of obtaining the direction from receiver to LED and using weighted triangulation method to locate receiver position. We admit the solution under the redefined problem is not optimal to the actual problem. Yet, our given solution is better to that in [1] due to the existence of noise, which is reasonable and has been verified.

Body Area Network (BAN) technology is related to many applications inside, on and around the human body. The basic configuration of a BAN is a set of sensors, which are wearable or are placed inside the human body, transmitting signals to a terminal situated in a doctor’s office, in order to assess or monitor some aspect of a patient’s physical condition. Additionally, in many BAN applications the information about the sensor location is very important, since without knowing a sensor’s location, the transmitted data may be of limited value. As an example, Wireless Video Capsule Endoscopy (VCE) can benefit greatly from the addition of location information. The capsule transmits an RF signal from inside the human body to another sensor on the body surface or external. From the image data provided by the capsule, taken together with the location information, the doctor can locate the infection or lesion and initiate appropriate medical care. In this way, the treatment can be more effective and accurate. In this thesis we investigate the characteristics of Ultra-Wide Band (UWB) RF propagation for BAN devices placed around and inside the human body. We have made measurements around the human body and around a water-filled phantom using an E8363B Vector Network Analyzer (VNA), specifically measuring the S21 signal, which gives the transfer function. Based on these measurement results, we discuss the channel propagation for cases where the transmitter and the receiver are on the surface of the body and analyze the UWB propagation characteristics for RF localization. Because it is impractical or even impossible to make measurements inside the human body, we chose to apply the measurements using a simulation model of homogenous tissue, which serves as an approximation of the signal propagation environment inside the body. First, by comparing the multipath situation in free space and within a model of homogenous tissue, we are able to analyze the multipath effects inside human body. Then, because of the different characteristics of RF propagation in different bandwidths, we have made measurements at UWB (3GHz to 10GHz), and narrowband (402MHz) frequencies.

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Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator.

Topographical orientation is the ability to orient oneself within the environment and to navigate through it to specific destinations. Topographical disorientation (TD) refers to deficits in orientation and navigation in the real environment, and is a common sequela of brain injuries. People with TD often have difficulties interacting with and perceiving the surrounding environment. The literature suggests that patients with TD are likely to benefit from research leading to clinical standards of practice and technology to facilitate topographical orientation. In the light of the above, the objectives of this thesis were to investigate methods of realizing a context-aware, wearable mediated environment system for indoor navigation, and to develop a standard method of quantifying the impact of such a system on indoor navigation task performance. In realizing these objectives, we first conducted an extensive literature review of in-door localization systems. This review served to identify potential technologies for an indoor, in-situ wayfinding assistive device. Subsequently, an automated navigation algorithm was designed. Our algorithm reduced the navigational effort of simulated patients with topographical disorientation while accounting for the physical abilities of the patient, environmental barriers and dynamic building changes. We introduced and demonstrated a novel energy-based wayfinding metric, which is independent of route complexity. An experiment was conducted to identify preferred graphical navigation tools for mediated reality wayfinding guidance. Different combinations of spatial knowledge, graphical presentations and reference frames were considered in the experiment. The data suggested that the locator and minimap are the preferred navigational tools. Two unique optical-inertial localization systems for real-time indoor human tracking were created. The first localization system was oriented to pedestrians, while the second was implemented on a wheelchair. Empirical tests produced localization accuracies comparable to those reported in literature. Finally, a fully operational mediated reality location aware system for indoor navigation was realized. Tests with human participants indicated a significant reduction in physical effort in comparison to the no-tool condition, during wayfinding tasks in an unfamiliar indoor environment. Collectively, the findings and developments of this thesis lay the foundation for future research on wearable, location-based navigational assistance for individuals with wayfinding difficulties.

Topographical orientation is the ability to orient oneself within the environment and to navigate through it to specific destinations. Topographical disorientation (TD) refers to deficits in orientation and navigation in the real environment, and is a common sequela of brain injuries. People with TD often have difficulties interacting with and perceiving the surrounding environment. The literature suggests that patients with TD are likely to benefit from research leading to clinical standards of practice and technology to facilitate topographical orientation. In the light of the above, the objectives of this thesis were to investigate methods of realizing a context-aware, wearable mediated environment system for indoor navigation, and to develop a standard method of quantifying the impact of such a system on indoor navigation task performance. In realizing these objectives, we first conducted an extensive literature review of in-door localization systems. This review served to identify potential technologies for an indoor, in-situ wayfinding assistive device. Subsequently, an automated navigation algorithm was designed. Our algorithm reduced the navigational effort of simulated patients with topographical disorientation while accounting for the physical abilities of the patient, environmental barriers and dynamic building changes. We introduced and demonstrated a novel energy-based wayfinding metric, which is independent of route complexity. An experiment was conducted to identify preferred graphical navigation tools for mediated reality wayfinding guidance. Different combinations of spatial knowledge, graphical presentations and reference frames were considered in the experiment. The data suggested that the locator and minimap are the preferred navigational tools. Two unique optical-inertial localization systems for real-time indoor human tracking were created. The first localization system was oriented to pedestrians, while the second was implemented on a wheelchair. Empirical tests produced localization accuracies comparable to those reported in literature. Finally, a fully operational mediated reality location aware system for indoor navigation was realized. Tests with human participants indicated a significant reduction in physical effort in comparison to the no-tool condition, during wayfinding tasks in an unfamiliar indoor environment. Collectively, the findings and developments of this thesis lay the foundation for future research on wearable, location-based navigational assistance for individuals with wayfinding difficulties.