Location tracking is of paramount importance to many applications such as healthcare, retail and navigation. Outdoor tracking can be easily implemented using the Global Positioning System (GPS). However, indoor tracking has been a difficult problem to tackle because GPS requires the line of sight to the satellites and therefore it does not work well in indoor environments. In this thesis, a high-precision indoor tracking system is proposed to identify, locate and track a person in an indoor room at a low cost. The proposed tracking system consists of three components: StepscanTM tiles, RFID and Kalman-filter based prediction. The StepscanTM tiles can generate precise location information. However, using StepscanTM tiles only in an indoor tracking system is too expensive because the manufacturing cost of each StepscanTM tile is very high. In the proposed system, StepscanTM tiles are deployed to cover a part of the indoor floor while RFID provides a full coverage. The location information from StepscanTM tiles and RFID is then used as inputs for our innovative prediction algorithm based on the Kalman filter, which consequently generates high-precision tracking results. The performance of the proposed system is investigated through extensive simulations. Our simulation results indicate that the proposed system increases the capability to track and locate a person by at least 24% (more than 50% in some cases), with errors ranging from 2.5% to 15%. Furthermore, the proposed system helps to reduce the cost of indoor tracking significantly. In terms of the number of StepscanTM tiles deployed in the system, a reduction of 7 to 25 tiles can be achieved in the scenarios under investigation. In terms of monetary cost, $21,000 to $75,000 can be saved for an indoor tracking system considered in our research.

Finding ones way in unknown and unfamiliar environments is a common task. A number of tools ranging from paper maps to location-based services have been introduced to assist human navigation. Undoubtedly, car navigation systems can be considered the most successful example of location based services that widely gained user acceptance. However the concept of car navigation is not always (perhaps rarely) suitable for pedestrian navigation. Moreover, precise localization of moving objects indoors is not possible due to the absence of an absolute positioning method such as GPS. These make accurate indoor tracking and navigation an interesting problem to explore. Many of the methods of spatial analysis popular in outdoor applications can be used indoors. In particular, generation of the indoor navigation network can be an effective solution for a) improving the navigation experience inside complex indoor structures and b) enhancing the analysis of the indoor tracking data collected with existing positioning solutions. Such building models should be based on a graph representation and consist of the number of ‘nodes’ and ‘edges’, where ‘nodes’ correspond to the central position of the room and ‘edge’ represents the medial axis of the hallway polygons, which physically connects these rooms. Similar node-links should be applied stairs and elevators to connect building floors. To generate this model, I selected the campus of University of Saskatchewan as the study area and presented a method that creates an indoor navigation network using ESRI ArcGIS products. First, the proposed method automatically extracts geometry and topology of campus buildings and computes the distances among all entities to calculate the shortest path between them. The system navigates through the University campus and it helps locating classrooms, offices, or facilities. The calculation of the route is based on the Dijkstra algorithm, but could employ any network navigation algorithm. To show the advantage of the generated network, I present results of a study conducted in conjunction with the department of Computer Science. An experiment that included 37 participants was designed to collect the tracking data on a university campus to demonstrate how the incorporation of the indoor navigation model can improve the analysis of the indoor movement data. Based on the results of the study, it can be concluded that the generated indoor network can be applied to raw positioning data in order to improve accuracy, as well as be employed as a stand-alone tool for enhancing of the route guidance on a university campus, and by extension any large indoor space consisting of individual or multiple buildings.

This study investigates augmented reality using third person perspective and what precision can be achieved by using this method. There have been prior studies in regards to precision using augmented reality, however studies using third person perspective is scarce. This study presents a solution using a static camera capturing the user as well as the plane to augment from behind. This augmented image is then transferred to a handheld device that is held by the user. Using this method the user will be free to manipulate and work with the plane without removing the device that captures the scene, a common problem when using visual reference markers in augmented reality. The study successfully shows that this can be implemented without compromising user experience as well as achieving a precision below 13 millimeters. The AR-tool has been proven to reduce time consumption of the task by up to four times compared to the manual method using a folding ruler.

Significant research efforts have been directed into smart home environments in the last decade creating abundant opportunities for the broader home services ecosystem to foster a wide range of innovative services. Research interest has been given on automatic identification and tracking of people within the home environment to support customised services such as care services for elderly and disadvantaged people to enable and prolong their independent living. Although various approaches have been proposed to tackle this problem, solutions still remain elusive due to various reasons (e.g. user acceptance). Literature reviews have indicated the need for an advanced non-tagged identification and tracking approach that is capable to provide the infrastructure support for realisation of context-aware services, satisfy users’ needs, and deal with the complexity of smart home environmental conditions. The aim of this study is to develop and implement an advanced approach that is capable to accurately detect, identify, and track people within opportune and calm home environment to be used as infrastructure for various application domains such as assisted living, healthcare, security and energy management. Accordingly, a novel multimodal approach for non-tagged human identification and tracking within home environment is proposed. The proposed approach combined floor pressure and PIR sensors through unique designed integration strategy aiming to merge the advantages of the two sensor types and overcome or minimise their weaknesses. The designed strategy enabled the PIR output signal pattern to afford explicit information indicating a person’s body surface area (size/shape). This information enhanced the identification accuracy, facilitated the custom designed smart floor, and reduced the overall cost. The conceptual framework of the proposed approach/strategy encompassed two key stages, hardware system design and implementation, and data processing. The hardware system design included the custom designed PIR and smart floor units. A test bed was designed and implemented for supporting the research studies, including proof of concept, concept demonstration, experimental and test cases studies. Data processing system has divided into different stages to accomplish the identification and tracking goals. First, the interested patterns were segmented and generated with threshold edge detection method and advanced pattern generation algorithm respectively. Second, limited set of features were extracted and selected from each pattern including ground reaction force GRF, gait, and body size/shape (PIR) features. Third, these features were merged at different fusion level, namely, feature-level and decision-level to provide comprehensive description about the person’s identity. Fourth, MLPNN multiclass classifier was adopted to process the feature vectors and recognise the person’s identity. Finally, the footstep patterns were tracked using weighted centroid tracking technique, in addition to MLPNN classifier to handle the footsteps association problems. Four test cases were designed and carried out to demonstrate, test, and evaluate the feasibility and effectiveness of the proposed non-tagged identification and tracking strategies/approach. The assessment outcomes have shown the potential of the proposed multimodal approach as an advanced strategy for implementation of an indoor non-tagged human identification and tracking system and to be used as infrastructure for supporting the delivery of various types of smart services within the smart home environments. In summary, the proposed multimodal approach has the potential to: (1) Identify up to 5 persons successfully with minimum 98.8% correct classification rate without tag, (2) detect, locate, and track multiple persons successfully without tag and the location error no more than 11.76 cm, approximately 1.5 times better in accuracy than the original set target (i.e. 30 cm), and (3) able to handle various tracking difficulties and solve 97.5% of data association problems.

As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

Indiana University-Purdue University Indianapolis (IUPUI) / Indoor tracking is a challenging problem. The level of accepted error is on a much smaller scale than that of its outdoor counterpart. While the global positioning system has become omnipresent, and a widely accepted outdoor tracking system it has limitations in indoor environments due to loss or degradation of signal. Many attempts have been made to address this challenge, but currently none have proven to be the de-facto standard. In this thesis, we introduce the concept of opportunistic tracking in which tracking takes place with whatever sensing infrastructure is present – static or mobile, within a given indoor environment. In this approach many of the challenges (e.g., high cost, infeasible infrastructure deployment, etc.) that prohibit usage of existing systems in typical application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges do still exist when it comes to provide an accurate positional estimate of an entities location in an indoor environment, namely: sensor classification, sensor selection, and multi-sensor data fusion. We propose an enhanced tracking framework that through the infusion of QoS-based selection criteria of trust and reliability we can improve the overall accuracy of the tracking estimate. This improvement is predicated on the introduction of learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly identified and evaluated based upon their specific behavioral characteristics through performance evaluation. This in-depth evaluation of sensors provides the basis for improving the sensor selection process. A side effect of obtaining this improved accuracy is the cost, found in the form of system runtime. This thesis provides a solution for this tradeoff between accuracy and cost through an optimization function that analyzes this tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an object as it moves indoors. We demonstrate that through this improved sensor classification, selection, data fusion, and tradeoff optimization we can provide an improvement, in terms of accuracy, over other existing indoor tracking systems.

No description available.

abstract: Unmanned aerial vehicles (UAVs) are widely used in many applications because of their small size, great mobility and hover performance. This has been a consequence of the fast development of electronics, cheap lightweight flight controllers for accurate positioning and cameras. This thesis describes modeling, control and design of an oblique-cross-quadcopter platform for indoor-environments. One contribution of the work was the design of a new printed-circuit-board (PCB) flight controller (called MARK3). Key features/capabilities are as follows: (1) a Teensy 3.2 microcontroller with 168MHz overclock –used for communications, full-state estimation and inner-outer loop hierarchical rate-angle-speed-position control, (2) an on-board MEMS inertial-measurement-unit (IMU) which includes an LSM303D (3DOF-accelerometer and magnetometer), an L3GD20 (3DOF-gyroscope) and a BMP180 (barometer) for attitude estimation (barometer/magnetometer not used), (3) 6 pulse-width-modulator (PWM) output pins supports up to 6 rotors (4) 8 PWM input pins support up to 8-channel 2.4 GHz transmitter/receiver for manual control, (5) 2 5V servo extension outputs for other requirements (e.g. gimbals), (6) 2 universal-asynchronous-receiver-transmitter (UART) serial ports - used by flight controller to process data from Xbee; can be used for accepting outer-loop position commands from NVIDIA TX2 (future work), (7) 1 I2C-serial-protocol two-wire port for additional modules (used to read data from IMU at 400 Hz), (8) a 20-pin port for Xbee telemetry module connection; permits Xbee transceiver on desktop PC to send position/attitude commands to Xbee transceiver on quadcopter. The quadcopter platform consists of the new MARK3 PCB Flight Controller, an ATG-250 carbon-fiber frame (250 mm), a DJI Snail propulsion-system (brushless-three-phase-motor, electronic-speed-controller (ESC) and propeller), an HTC VIVE Tracker and RadioLink R9DS 9-Channel 2.4GHz Receiver. This platform is completely compatible with the HTC VIVE Tracking System (HVTS) which has 7ms latency, submillimeter accuracy and a much lower price compared to other millimeter-level tracking systems. The thesis describes nonlinear and linear modeling of the quadcopter’s 6DOF rigid-body dynamics and brushless-motor-actuator dynamics. These are used for hierarchical-classical-control-law development near hover. The HVTS was used to demonstrate precision hover-control and path-following. Simulation and measured flight-data are shown to be similar. This work provides a foundation for future precision multi-quadcopter formation-flight-control. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2018

A large part of people's lives are spent in indoor spaces such as office and university buildings, shopping malls, subway stations, airports, museums, community centers, etc. Such kind of spaces can be very large and paths inside the locations can be constrained and complex. Deployment of indoor tracking technologies like RFID, Bluetooth, and Wi-Fi can track people and object movements from one symbolic location to another within the indoor spaces. The resulting tracking data can be massive in volume. Analyzing these large volumes of tracking data can reveal interesting patterns that can provide opportunities for different types of location-based services, security, indoor navigation, identifying problems in the system, and finally service improvements. In addition to the huge volume, the structure of the unprocessed raw tracking data is complex in nature and not directly suitable for further efficient analysis. It is essential to develop efficient data management techniques and perform different kinds of analysis to make the data beneficial to the end user. The Ph.D. study is sponsored by the BagTrack Project (http://daisy.aau.dk/bagtrack). The main technological objective of this project is to build a global IT solution to significantly improve the worldwide aviation baggage handling quality. The Ph.D. study focuses on developing data management techniques for efficient and effective analysis of RFID-based symbolic indoor tracking data, especially for the baggage tracking scenario. First, the thesis describes a carefully designed a data warehouse solution with a relational schema sitting underneath a multidimensional data cube, that can handle the many complexities in the massive non-traditional RFID baggage tracking data. The thesis presents the ETL flow that loads the data warehouse with the appropriate tracking data from the data sources. Second, the thesis presents a methodology for mining risk factors in RFID baggage tracking data. The aim is to find the factors and interesting patterns that are responsible for baggage mishandling. Third, the thesis presents an online risk prediction technique for indoor moving objects. The target is to develop a risk prediction system that can predict the risk of an object in real-time during its operation so that the object can be saved from being mishandled. Fourth, the thesis presents two graph-based models for constrained and semi-constrained indoor movements, respectively. These models are used for mapping the tracking records into mapping records that represent the entry and exit times of an object at a symbolic location. The mapping records are then used for finding dense locations. Fifth, the thesis presents an efficient indexing technique, called the $DLT$-Index, for efficiently processing dense location queries as well as point and interval queries. The outcome of the thesis can contribute to the aviation industry for efficiently processing different analytical queries, finding problems in baggage management systems, and improving baggage handling quality. The developed data management techniques also contribute to the spatio-temporal data management and data mining field. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished