Optimizing Deployment and Maintenance of Indoor Localization Systems

Pervasive computing envisions the achievement of seamless and distraction-free support for tasks by means of context-aware applications. Context can be defined as the information which can be used to characterize the situation of an entity such as persons or objects which are relevant for the behaviour of an application. A context-aware application is one which can adapt its functionality based on changes in the context of the user or entity. Location is an important piece of context because a lot of information can be inferred about the situation of an entity just by knowing where it is. This makes location very useful for many context-aware applications. In outdoor scenarios, the Global Positioning System (GPS) is used for acquiring location information. However, GPS signals are relatively weak and do not penetrate buildings well, rendering them less than suitable for location estimation in indoor environments. However, people spend most of their time in indoor locations and therefore it is necessary to have location systems which would work in these scenarios. In the last two decades, there has been a lot of research into and development of indoor localization systems. A wide range of technologies have been applied in the development of these systems ranging from vision-based systems, sound-based systems as well as Radio Frequency (RF) signal based systems. In a typical indoor localization system deployment, an indoor environment is setup with different signal sources and then the distribution of the signals in the environment is recorded in a process known as calibration. The distribution of signals, also known as a radio map, is then later employed to estimate location of users by matching their signal observations to the radio map. However, not all the different signal technologies and approaches provide the right balance of accuracy, precision and cost to be suitable for most real world deployment scenarios. Of the different RF signal technologies, WLAN and Bluetooth based indoor localization systems are the most common due to the ubiquity of the signal deployments for communication purposes, and the accessibility of compatible mobile computing devices to the users of the system. Many of the indoor localization systems have been developed under laboratory conditions or only with small-scale controlled indoor areas taken into account. This poses a challenge when transposing these systems to real-world indoor environments which can be rather large and dynamic, thereby significantly raising the cost, effort and practicality of the deployment. Furthermore, due to the fact that indoor environments are rarely static, changes in the environment such as moving of furniture or changes in the building layout could adversely impact the performance of the localization system deployment. The system would then need to be recalibrated to the new environmental conditions in order to achieve and maintain optimal localization performance in the indoor environment. If this happens regularly, it can significantly increase the cost and effort for maintenance of the indoor localization system over time. In order to address these issues, this dissertation develops methods for more efficient deployment and maintenance of the indoor localization systems. A localization system deployment consists of three main phases; setup and calibration, localization and maintenance. The main contributions of this dissertation are proposed optimizations to the different stages of the localization system deployment lifecycle. First, the focus is on optimizing setup and calibration of fingerprinting-based indoor localization systems. A new method for dense and efficient calibration of the indoor environmental areas is proposed, with minimal effort and consequently reduced cost. During calibration, the signal distribution in the indoor environment is distorted by the presence of the person doing the calibration. This leads to a radio map which is not a very accurate representation of the environment. Therefore a model for WLAN signal attenuation by the human body is proposed in this dissertation. The model captures the pattern of change to the signal due the presence of the human body in the signal path. By applying the model, we can compensate for the attenuation caused by the person and thereby generate a more accurate map of the signal distribution in the environment. A more precise signal distribution leads to better precision during location estimation. Secondly, some optimizations to the localization phase are presented. The dense fingerprints of the environment created during the setup phase are used for generating location estimates by matching the captured signal distribution with the pre-recorded distribution in the environment. However, the location estimates can be further refined given additional context information. This approach makes use of sensor fusion and ambient intelligence in order to improve the accuracy of the location estimates. The ambient intelligence can be gotten from smart environments such as smart homes or offices, which trigger events that can be applied to location estimation. These optimizations are especially useful for indoor tracking applications where continuous location estimation and accurate high frequency location updates are critical. Lastly, two methods for autonomous recalibration of localization systems are presented as optimizations to the maintenance phase of the deployment. One approach is based on using the localization system infrastructure to monitor the signal characteristic distribution in the environment. The results from the monitoring are used by the system to recalibrate the signal distribution map as needed. The second approach evaluates the Received Signal Strength Indicator (RSSI) of the signals as measured by the devices using the localization system. An algorithm for detecting signal displacements and changes in the distribution is proposed, as well as an approach for subsequently applying the measurements to update the radio map. By constantly self-evaluating and recalibrating the system, it is possible to maintain the system over time by limiting the degradation of the localization performance. It is demonstrated that the proposed approach achieves results comparable to those obtained by manual calibration of the system. The above optimizations to the different stages of the localization deployment lifecycle serve to reduce the effort and cost of running the system while increasing the accuracy and reliability. These optimizations can be applied individually or together depending on the scenario and the localization system considered

Doctor of Philosophy

dissertationDevice-free localization (DFL) and tracking services are important components in security, emergency response, home and building automation, and assisted living applications where an action is taken based on a person's location. In this dissertation, we develop new methods and models to enable and improve DFL in a variety of radio frequency sensor network configurations. In the first contribution of this work, we develop a linear regression and line stabbing method which use a history of line crossing measurements to estimate the track of a person walking through a wireless network. Our methods provide an alternative approach to DFL in wireless networks where the number of nodes that can communicate with each other in a wireless network is limited and traditional DFL methods are ill-suited. We then present new methods that enable through-wall DFL when nodes in the network are in motion. We demonstrate that we can detect when a person crosses between ultra-wideband radios in motion based on changes in the energy contained in the first few nanoseconds of a measured channel impulse response. Through experimental testing, we show how our methods can localize a person through walls with transceivers in motion. Next, we develop new algorithms to localize boundary crossings when a person crosses between multiple nodes simultaneously. We experimentally evaluate our algorithms with received signal strength (RSS) measurements collected from a row of radio frequency (RF) nodes placed along a boundary and show that our algorithms achieve orders of magnitude better localization classification than baseline DFL methods. We then present a way to improve the models used in through-wall radio tomographic imaging with E-shaped patch antennas we develop and fabricate which remain tuned even when placed against a dielectric. Through experimentation, we demonstrate the E-shaped patch antennas lower localization error by 44% compared with omnidirectional and microstrip patch antennas. In our final contribution, we develop a new mixture model that relates a link's RSS as a function of a person's location in a wireless network. We develop new localization methods that compute the probabilities of a person occupying a location based on our mixture model. Our methods continuously recalibrate the model to achieve a low localization error even in changing environments

Off-line evaluation of mobile-centric indoor positioning systems: the experiences from the 2017 IPIN competition

The development of indoor positioning solutions using smartphones is a growing activity with an enormous potential for everyday life and professional applications. The research activities on this topic concentrate on the development of new positioning solutions that are tested in specific environments under their own evaluation metrics. To explore the real positioning quality of smartphone-based solutions and their capabilities for seamlessly adapting to different scenarios, it is needed to find fair evaluation frameworks. The design of competitions using extensive pre-recorded datasets is a valid way to generate open data for comparing the different solutions created by research teams. In this paper, we discuss the details of the 2017 IPIN indoor localization competition, the different datasets created, the teams participating in the event, and the results they obtained. We compare these results with other competition-based approaches (Microsoft and Perf-loc) and on-line evaluation web sites. The lessons learned by organising these competitions and the benefits for the community are addressed along the paper. Our analysis paves the way for future developments on the standardization of evaluations and for creating a widely-adopted benchmark strategy for researchers and companies in the field.We would like to thank Topcon Corporation for sponsoring the competition track with an award for the winning team. We are also grateful to Francesco Potorti, Sangjoon Park, Hideo Makino, Nobuo Kawaguchi, Takeshi Kurata and Jesus Urena for their invaluable help in organizing and promoting the IPIN competition and conference. Many thanks to Raul Montoliu, Emilio Sansano, Marina Granel and Luis Alisandra for collecting the databases in the UJITI building. Parts of this work were carried out with the financial support received from projects and grants: REPNIN network (TEC2015-71426-REDT), LORIS (TIN2012-38080-C04-04), TARSIUS (TIN2015-71564-C4-2-R (MINECO/FEDER)), SmartLoc (CSIC-PIE Ref. 201450E011), "Metodologias avanzadas para el diseno, desarrollo, evaluacion e integracion de algoritmos de localizacion en interiores" (TIN2015-70202-P), GEO-C (Project ID: 642332, H2020-MSCA-ITN-2014-Marie Sklodowska-Curie Action: Innovative Training Networks), and financial support from the Ministry of Science and Technology, Taiwan (106-3114-E-007-005 and 105-2221-E-155-013-MY3). The HFTS team has been supported in the frame of the German Federal Ministry of Education and Research programme "FHprofUnt2013" under contract 03FH035PB3 (Project SPIRIT). The UMinho team has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT-Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. G.M. Mendoza-Silva gratefully acknowledges funding from grant PREDOC/2016/55 by Universitat Jaume I.info:eu-repo/semantics/publishedVersio

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Infrared ranging in multipath environments for indoor localization of mobile targets

Esta tesis aborda el problema de la medida de diferencias de distancia mediante señales ópticas afectadas por multicamino, aplicada a la localización de agentes móviles en espacios interiores. Los avances en robótica, entornos inteligentes y vehículos autónomos han creado un campo de aplicación específico para la localización en interiores, cuyos requerimientos de precisión (en el rango de los cm) son muy superiores a los demandados por las aplicaciones de localización orientadas a personas, en cuyo contexto se han desarrollado la mayor parte de las alternativas tecnológicas. La investigación con métodos de geometría proyectiva basados en cámaras y de multilateración basados en medida de distancia con señales de radiofrecuencia de banda ancha, de ultrasonido y ópticas han demostrado un rendimiento potencial adecuado para cubrir estos requerimientos. Sin embargo, todas estas alternativas, aún en fase de investigación, presentan dificultades que limitan su aplicación práctica. En el caso de los sistemas ópticos, escasamente estudiados en este contexto, los trabajos previos se han basado en medidas de diferencia de fase de llegada de señales infrarrojas moduladas sinusoidalmente en intensidad. Una infraestructura centralizada computa medidas diferenciales, entre receptores fijos, de la señal emitida desde el móvil a posicionar, y calcula la posición del móvil mediante trilateración hiperbólica a partir de éstas. Estas investigaciones demostraron que se pueden alcanzar precisiones de pocos centímetros; sin embargo, las interferencias por multicamino debidas a la reflexión de la señal óptica en superficies del entorno pueden degradar esta precisión hasta las decenas de centímetros dependiendo de las características del espacio. Así pues, el efecto del multicamino es actualmente la principal fuente de error en esta tecnología, y por tanto, la principal barrera a superar para su implementación en situaciones reales. En esta tesis se propone y analiza un sistema de medida con señales ópticas que permite obtener estimaciones de diferencias de distancia precisas reduciendo el efecto crítico del multicamino. El sistema propuesto introduce una modulación con secuencias de ruido pseudoaleatorio sobre la modulación sinusoidal típicamente usada para medida de fase por onda continua, y aprovecha las propiedades de ensanchamiento en frecuencia de estas secuencias para reducir el efecto del multicamino. El sistema, que realiza una doble estimación de tiempo y fase de llegada, está compuesto por una etapa de sincronización que posibilita la demodulación parcialmente coherente de la señal recibida, seguida de un medidor diferencial de fase sobre las componentes desensanchadas tras la demodulación. Las condiciones de multicamino óptico típicas en espacios interiores, con una componente de camino directo claramente dominante, permiten que el proceso de demodulación recupere más potencia del camino directo que del resto de contribuciones, reduciendo el efecto del multicamino en la estimación final. Los resultados obtenidos demuestran que la aplicación del método propuesto permitiría realizar posicionamiento a partir de señales ópticas con el rendimiento adecuando para aplicaciones de robótica y guiado de vehículos en espacios interiores; además, el progresivo aumento de la potencia y el ancho de banda de los dispositivos optoelectrónicos disponibles permite esperar un incremento considerable de las prestaciones de la propuesta en los próximos años

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research

Indoor localization utilizing existing infrastructure in smart homes : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer and Electronics Engineering, Massey University, Albany, New Zealand

Listed in 2019 Dean's List of Exceptional ThesesIndoor positioning system (IPS) have received significant interest from the research community over the past decade. However, this has not eventuated into widespread adoption of IPS and few commercial solutions exist. Integration into Smart Homes could allow for secondary services including location-based services, targeted user experiences and intrusion detection, to be enabled using the existing underlying infrastructure. Since New Zealand has an aging population, we must ensure that the elderly are well looked after. An IPS solution could detect whether a person has been immobile for an extended period and alert medical personnel. A major shortcoming of existing IPS is their reliance on end-users to undertake a significant infrastructure investment to facilitate the localization tasks. An IPS that does not require extensive installation and calibration procedures, could potentially see significant uptake from end users. In order to expedite the widespread adoption of IPS technology, this thesis focuses on four major areas of improvement, namely: infrastructure reuse, reduced node density, algorithm improvement and reduced end user calibration requirements. The work presented demonstrates the feasibility of utilizing existing wireless and lighting infrastructure for positioning and implements novel spring-relaxation and potential fields-based localization approaches that allow for robust target tracking, with minimal calibration requirements. The developed novel localization algorithms are benchmarked against the existing state of the art and show superior performance

Enhanced indoor positioning utilising wi-fi fingerprint and QR calibration techniques

The growing interest in location-based services (LBS), due to the demand for its application in personal navigation, billing and information enquiries, has expedited the research development for indoor positioning techniques. The widely used global positioning system (GPS) is a proven technology for positioning, navigation, but it performs poorly indoors. Hence, researchers seek alternative solutions, including the concept of signal of opportunity (SoOP) for indoor positioning. This research planned to use cheap solutions by utilizing available communication system infrastructure without the need to deploy new transmitters or beacons for positioning purposes. Wi-Fi fingerprinting has been identified for potential indoor positioning due to its availability in most buildings. In unplanned building conditions where the available number of APs is limited and the locations of APs are predesignated, certain positioning algorithms do not perform well consistently. In addition, there are several other factors that influence positioning accuracy, such as different path movements of users and different Wi-Fi chipset manufacturers. To overcome these challenges, many techniques have been proposed, such as collaborative positioning techniques, data fusion of radio-based positioning and mobile-based positioning that uses sensors to sense the physical movement activity of users. A few researchers have proposed combining radio-based positioning with vision-based positioning while utilizing image sensors. This work proposed integrated layers of positioning techniques, which is based on enhanced deterministic method; Bayesian estimation and Kalman filter utilising dynamic localisation region. Here, accumulated accuracy is proposed with distribution of error location by estimation at each test point on path movement. The error distribution and accumulated accuracy have been presented in graphs and tables for each result. The proposed algorithm has been enhanced by location based calibration with additional QR calibration. It allows not only correction of the actual position but the control of the errors from being accumulated by utilizing the Bayesian technique and dynamic localisation region. The position of calibration point is determined by analysing the error distribution region. In the last part, modification on Kalman filter step for calibration algorithm did further improve the location error compared to other deterministic algorithms with calibration point. The CDF plots have shown all developed techniques that provide accuracy improvement for indoor positioning based on Wi-Fi fingerprinting and QR calibration