The Global Positioning System (GPS) is the most popular positioning system among some operational Global Navigation Satellite Systems (GNSS). However, GNSS suffer from accuracy deterioration and interruption of services in dense urban areas and are almost unavailable indoors. Although high-sensitivity receivers improve signal acquisition indoors, multipath is still be a challenging problem that affects accuracy especially indoors where a direct line of sight between transmitter and receiver almost never exist. Moreover, the wireless signal features are significantly jeopardized by obstacles and constructions indoors. To address these challenges, this research came in the context of proposing an alternative positioning system that is designed for GPS-denied environment and especially for indoors. Cramer-Rao Lower-Bound (CRLB) analysis was used to estimate the lower bound accuracy of different positioning methods indoors. Based on CRLB analysis, this research approached the wireless positioning problem indoors utilizing received signal strength (RSS) to achieve the following: 1) Developing new estimation methods to model the wireless RSS patterns in indoors. 2) Designing adaptive RSS-based wireless positioning methods for indoors. 3) Establishing a consistent framework for indoor wireless positioning systems. 4) Developing new methods to integrate inertial/odometer-based navigation systems with the developed wireless positioning methods for further improvements. The theoretical basis of the work was built on nonlinear stochastic estimation techniques including Particle Filtering, Gaussian Process Regression, Fast Orthogonal Search, Least-Squares, and Radial Basis Functions Neural Networks. All the proposed wireless positioning methods were developed and physically realized on Android-based smart-phones using the IEEE 802.11 WLANs (WiFi). In addition, successful integration with inertial/odometer sensors of mobile robots has been performed on embedded systems. Both theoretical analysis and experimental results showed significant improvements in modeling RSS indoors dynamically without offline training achieving a positioning accuracy of 1-3 meters. Sub-meter accuracy was achieved via integration with inertial/odometer sensors. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-03-25 16:11:59.518

The projects goal was to make an already existing indoor positioning system useful forthe average smartphone user. This was achieved by creating an Android applicationwhich, along with a running server, continuously presents the user with all the currentneeded information. It uses the graphics engine andEngine to create the graphical userinterface. The application was created in the development environments Eclipse andAndroid Studio.

GIS are geoprocessing programs that are commonly used to store and perform calculations on terrain data, maps, and other geospatial data. GIS offers the latest terrain and building data as well as tools to process this data. This thesis considers three applications of GIS data and software: a Large Scale Radio Frequency (RF) Model, a Medium Scale RF Model, and Indoor Positioning. The Large Scale RF Model estimates RF propagation using the latest terrain data supplied in GIS for frequencies ranging from 500 MHz to 5 GHz. The Medium Scale RF Model incorporates GIS building data to model WiFi systems at 2.4 GHz for a range of up to 300m. Both Models can be used by city planners and government offcials, who commonly use GIS for other geospatial and geostatistical information, to plan wireless broadband systems using GIS. An Indoor Positioning Experiment is also conducted to see if apriori knowledge of a building size, location, shape, and number of floors can aid in the RF geolocation of a target indoors. The experiment shows that correction of a target to within a building's boundaries reduces the location error of the target, and the vertical error is reduced by nearly half. / Master of Science

The principal objectives of this research are: to investigate the performance of different fingerprint-based WiFi Indoor Positioning Systems (IPS), analyse historical long-term data signals, detection of signal change points and outliers; then present an enhanced method that generates temporal based fingerprints. The proposed method consists of analysing signal strength profiles over time and detecting points at which the profile behaviour changes. This methodology can be used to dynamically adjust the fingerprint based on environmental factors, and with this select the relevant Wireless Access Points (WAPs) to be used for fingerprinting. The use of an Exponentially Weighted Moving Average (EWMA) Control Chart is investigated for this purpose. A long-term analysis of the WiFi scenery is presented and used as a test-bed for evaluation of state-of-the-art fingerprinting techniques. Data was collected and analysed over a period of 18 months, with over 840 different WAPs detected in over 77,000 observations covering 47 different locations of varying characteristics. A fully functional IPS has been developed and the design and implementation is described in this thesis. The system allows the scanning and recording of WiFi signals in order to define the generation of temporal fingerprints that can create radio-maps, which then allow indoor positioning to occur. This thesis presents the theory behind the concept and develops the technology to create a testable implementation. Experiments and their evaluation are also included. Based on the timestamp experiments the proposed system shows there is still room level accuracy, with a reduction in radio-map size.

Large enterprises are often interested in tracking objects and people within buildings to improve resource allocation and occupant experience. Infrastructure-based indoor positioning systems (IIPS) can provide this service at low-cost by leveraging already deployed Wi-Fi infrastructure. Typically, IIPS perform localization and tracking of devices by measuring only Wi-Fi signals at wireless access points and do not rely on inertial sensor data at mobile devices (e.g., smartphones), which would require explicit user consent and sensing capabilities of the devices. Despite these advantages, building an economically viable cost-effective IIPS that can accurately and simultaneously track many devices over very large buildings is difficult due to three main challenges. First, Wi-Fi signal measurements are extremely noisy due to unpredictable multipath propagation and signal attenuation. Second, as the IIPS obtain measurements in a best effort manner without requiring any applications installed on a tracked device, the measurements are temporally sparse and non-periodic, which makes it difficult to exploit historical measurements. Third, the cost-effective IIPS have limited computational resources, in turn limiting scalability in terms of the number of simultaneously tracked devices. Prior approaches have narrowly focused on either improving the accuracy or reducing the complexity of localization algorithms. To compute the location at the current time step, they typically use only the latest explicit Wi-Fi measurements (e.g., signal strengths). The novelty of our approach lies in considering contexts of a device that can provide useful indications of the device's location. One such example of context is device motion. It indicates whether or not the device's location has changed. For a stationary device, the IIPS can either skip expensive device localization or aggregate noisy, temporally sparse location estimates to improve localization accuracy. Another example of context applicable to a moving device is a floor map that consists of pre-defined path segments that a user can take. The map can be leveraged to constrain noisy, temporally sparse location estimates on the paths. The thesis of this dissertation is that embedding context-aware capabilities in the IIPS enhances its performance in tracking many devices simultaneously and accurately. Specifically, we develop motion detection and map matching to show the benefits of leveraging two critical contexts: device motion and floor map. Providing motion detection and map matching is non-trivial in the IIPS where we must rely only on data from the Wi-Fi infrastructure. This thesis makes two contributions. First, we develop feature-based and deep learning-based motion detection models that exploit temporal patterns in Wi-Fi measurements across different access points to classify device motion in real time. Our extensive evaluations on datasets from real Wi-Fi deployments show that our motion detection models can detect device motion accurately. This, in turn, allows the IIPS to skip repeated location computation for stationary devices or improve the accuracy of localizing these devices. Second, we develop graph-based and image-based map matching models to exploit floor maps. The novelty of the graph-based approach lies in applying geometric and topological constraints to select which path segment to align the current location estimate. Our graph-based map matching can align a location estimate of a user device on the path taken by the user and close to the user's current location. The novelty of the image-based approach lies in representing for the first time, input data including location estimates and the floor map as 2D images. This novel representation enables the design, development, and application of encoder-decoder neural networks to exploit spatial relationships in input images to potentially improve location accuracy. In our evaluation, we show that the image-based approach can improve location accuracy with large simulated datasets, compared to the graph-based approach. Together, these contributions enable improvement of the IIPS in its ability to accurately and simultaneously track many devices over large buildings.

Over the last 20 years, navigation has almost become synonymous with satellite positioning, e.g. the Global Positioning System (GPS). On land, sea or in the air, on the road or in a city, knowing ones position is a question of getting a clear line of sight to enough satellites. Unfortunately, since the signals are extremely weak there are environments the GPS signals cannot reach but where positioning is still highly sought after, such as indoors and underwater. Also, because the signals are so weak, GPS is vulnerable to jamming. This thesis is about alternative means of positioning for three scenarios where gps cannot be used. Indoors, there is a desire to accurately position first responders, police officers and soldiers. This could make their work both safer and more efficient. In this thesis an inertial navigation system using a foot mounted inertial magnetic mea- surement unit is studied. For such systems, zero velocity updates can be used to significantly reduce the drift in distance travelled. Unfortunately, the estimated direction one is moving in is also subject to drift, causing large positioning errors. We have therefore chosen to throughly study the key problem of robustly estimating heading indoors. To measure heading, magnetic field measurements can be used as a compass. Unfortunately, they are often disturbed indoors making them unreliable. For estimation support, the turn rate of the sensor can be measured by a gyro but such sensors often have bias problems. In this work, we present two different approaches to estimate heading despite these shortcomings. Our first system uses a Kalman filter bank that recursively estimates if the magnetic readings are disturbed or undisturbed. Our second approach estimates the entire history of headings at once, by matching integrated gyro measurements to a vector of magnetic heading measurements. Large scale experiments are used to evaluate both methods. When the heading estimation is incorporated into our positioning system, experiments show that positioning errors are reduced significantly. We also present a probabilistic stand still detection framework based on accelerometer and gyro measurements. The second and third problems studied are both maritime. Naval navigation systems are today heavily dependent on GPS. Since GPS is easily jammed, the vessels are vulnerable in critical situations. In this work we describe a radar based backup positioning system to be used in case of GPS failure. radar scans are matched using visual features to detect how the surroundings have changed, thereby describing how the vessel has moved. Finally, we study the problem of underwater positioning, an environment gps signals cannot reach. A sensor network can track vessels using acoustics and the magnetic disturbances they induce. But in order to do so, the sensors themselves first have to be accurately positioned. We present a system that positions the sensors using a friendly vessel with a known magnetic signature and trajectory. Simulations show that by studying the magnetic disturbances that the vessel produces, the location of each sensor can be accurately estimated.

The demand of Indoor Location Based Services LBS has increased over the past years as smart phone market expands. As a result, there's a growing interest in developing efficient and reliable indoor positioning systems for mobile devices. Wi-Fi signal strength fingerprint-based approaches attract more and more attention due to the wide deployment of Wi-Fi access points. Indoor positioning problem using Wi-Fi signal fingerprints can be viewed as a machine learning task to be solved mathematically. This thesis proposes an efficient and reliable Wi-Fi real-time indoor positioning system using machine learning algorithms. The proposed positioning system, together with a location server equipped with the same algorithms, are tested and evaluated in several indoor scenarios. Simulation and testing results show that the proposed system is a feasible LBS solution.

The demand of Indoor Location Based Services LBS has increased over the past years as smart phone market expands. As a result, there's a growing interest in developing efficient and reliable indoor positioning systems for mobile devices. Wi-Fi signal strength fingerprint-based approaches attract more and more attention due to the wide deployment of Wi-Fi access points. Indoor positioning problem using Wi-Fi signal fingerprints can be viewed as a machine learning task to be solved mathematically. This thesis proposes an efficient and reliable Wi-Fi real-time indoor positioning system using machine learning algorithms. The proposed positioning system, together with a location server equipped with the same algorithms, are tested and evaluated in several indoor scenarios. Simulation and testing results show that the proposed system is a feasible LBS solution.

The development of an Indoor Positioning System that requires a non-invasive setup and installation process is outlined in this dissertation. The Hardware, Mechanical and Software components are described in complete detail. The system operates using a hybrid of Bluetooth Low Energy (BLE) signal strength analysis and proximity sensor data collection to determine the location of a known Bluetooth compatible device. Additionally, a dynamic remote calibration protocol was developed to ensure a safe and smooth setup and integration process in any location the system is implemented. The system uses custom designed beacon modules that connect directly to outlets in designated rooms. These beacons relay sensor and BLE data to a Hub module that collects and stores all this data locally and on a cloud server. These features ensured that the IPS is a completely remote device that can be setup independently by the user. To our knowledge, this is the only Indoor Positioning System that does not require prior knowledge of the location of integration and the need for an in-person setup and calibration process. Additionally, despite the lack of an extensive setup and calibration process the system still operates at an accurate room detection percentage of 98%. To further prove its ease of use the system has been implemented in a clinical study where several older adults (65+) have integrated this system within their homes. This system has been designed to act as the foundation for larger scale healthcare monitoring applications. / Thesis / Candidate in Philosophy / Indoor positioning technology acts as the foundation for several healthcare monitoring networks. An accurate and easy to use indoor positioning system will entail how effective the overall healthcare monitoring platform is. Additionally, indoor positioning itself can be accomplished in several different ways. Some of these approaches include the use of physical sensors to detect presence, signal strength approximations via some sort of communication protocol or even the use of secure entry via RFID identification tags. Currently, most of the systems that use one of these approaches require extensive setup and calibration processes and extensive knowledge of the tracking locations. However, this is not always practical especially when the system is integrated in a large-scale environment like a retirement home. A system with an easy- to-use setup and installation platform is needed to complete these high impact healthcare monitoring projects.

A technique is proposed to improve the accuracy of indoor positioning systems based on WIFI radio-frequency signals by using dynamic access points and fingerprints (DAFs). Moreover, an indoor position system that relies solely in DAFs is proposed. The walking pattern of indoor users is classified as dynamic or static for indoor positioning purposes. I demonstrate that the performance of a conventional indoor positioning system that uses static fingerprints can be enhanced by considering dynamic fingerprints and access points. The accuracy of the system is evaluated using four positioning algorithms and two random access point selection strategies. The system facilitates the location of people where there is no wireless local area network (WLAN) infrastructure deployed or where the WLAN infrastructure has been drastically affected, for example by natural disasters. The system can be used for search and rescue operations and for expanding the coverage of an indoor positioning system.