A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

Filtering Impulses in Dynamic Noise in the Presence of Large Measurement Noise

This work considers the problem of filtering a system in which the dynamic noise occasionally has an impulse value that is an order of magnitude or more larger than its typical expected distribu-tion. This is particularly challenging when the ratio of measurement noise to typical dynamic noise is large enough that the impulse dynamic noise cannot be easily distinguished from a large random occurrence of measurement noise. A new filter model is proposed using a multiple model approach in which one of the models is an impulse. The implementation of the model is demonstrated in a Kalman filter framework. Simulation results show the improvement of the new filter over existing methods across a range of measurement, typical, and impulse dynamic noises. The filter is then ap-plied to three different problems: 2D human motion tracking using ultra-wideband (UWB) position measurements, power system state estimation on a coupled bus, and handling outlier measurement noise in UWB tracking. In each case the new filter demonstrates a 2-4% improvement over existing state-of-the-art techniques

Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking

Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.Fil: Wang, Wenxu. Guandong University Of Technology; ChinaFil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australi

A Study of Environment Noise in Ultra-Wideband Indoor Position Tracking

This work is motivated by the problem of improving the accuracy of indoor ultra-wideband (UWB) position tracking through the study of the environment noise that affects such a system. Current systems can provide accuracy in the range of 30-100 cm in a small building, suitable for applications that require rough room-level precision such as asset tracking and surveillance. Our long-term goal is to improve the accuracy to 1 cm or better, expanding potential applications to telepresence, augmented reality, training and entertainment. This work investigates the possibility of systematically observing the measurement noise of an UWB position tracking system and building a map of it throughout a facility. In order to understand the effect of environment noise on UWB indoor positioning and in turn filter out the effects of this noise, it is important to have an idea of what this measurement noise looks like in a real world scenario. In this work, an understanding of the measurement noise is gained by taking many measurements using a commercially-available UWB positioning system installed in a real world scenario and analyzing these measurements in various ways. To the author\u27s knowledge, no one has used such an exhaustive approach to analyze measurement noise in UWB indoor positioning. The results of this work show that the measurement noise that affects a UWB indoor position tracking system can be effectively modeled using a weighted sum of Gaussians, is stable over time and is locally similar. Furthermore, a particle filter augmented with a measurement noise map is proposed to improve position tracking accuracy. Finally, a metric is proposed that can be used to quantify expected system performance based on sensor location, sensor orientation and facility floorplan. Using this metric, a procedure is developed to determine the parameters, i.e. sensor position, sensor orientation and potentially others, of the physical installation of the UWB tracking system that will produce minimum measurement error based on sensor geometry and physical facility constraints

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

Joint Pose and Radio Channel Estimation

This thesis investigates the combination of pose and radio channel estimation. Pose is the knowledge of the position and orientation of a device whereas the radio channel describes the transmission medium between radio transmitters and receivers. The two subjects are both active research topics with a long history of applications but there has to the author's knowledge been very little work published about combining the two areas using a sensor fusion framework. A well established approach for pose estimation is using an inertial measurement unit (IMU). Using an inexpensive IMU standalone for dead reckoning pose estimation is tempting but it is not a working solution due to noise and other imperfections in the IMU. There is also a fundamental limitation of inertial sensors, they can not, because of Galileo's principle, obtain any information about absolute velocity of the device. To obtain reliable pose estimates for a longer time, the measurements from the IMU must be fused with some other sensor information. This thesis shows how the pervasive electric magnetic fields from existing radio communication systems such as the cellular mobile systems GSM, 3G, or 4G can be used. Angle of arrival estimation using antenna arrays is a well studied problem with many different algorithms resolving the individual rays impinging on the array. However, less attention has been given to so called virtual array antennas where only one receiver element is used. By tracking the movement of the element, an array with properties similar to a stationary array with multiple elements is formed. By combining the IMU and the radio channel information, a map of the local radio environment can be obtained. At the same time, the map is used for adjusting for the errors in the IMU that lead to inaccurate pose estimates by using tightly coupled nonlinear state estimation algorithms from the sensor fusion framework. The goals for this thesis is to develop a dynamic model for kinematics and a ray-trace based radio channel model that can be used together with the particle filter for sensor fusion. It also contains an initial investigation of limitations and achievable performance for the joint pose and radio channel estimation problem, including radio imperfection such as thermal noise, and phase/frequency error. The proposed model is evaluated using both simulations and datasets from experiments. The analysis of the evaluation shows that the proposed model, together with sensor fusion algorithms, provides a breakthrough in pose estimation using a low cost IMU

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

A Safety System based on Bluetooth Low Energy (BLE) to prevent the misuse of Personal Protection Equipment (PPE) in construction

In this paper we address the issue of safety in the use of Personal Protection Equipment (PPE) in construction, industrial, or similar sites where power tools are used. We propose a novel solution that can control actively the power of the tool depending on the worker–tool distance. It is based on RSSI information transmitted by BLE devices arranged in a particular rig, combined with a Bayesian distance estimator. Such an approach minimizes the required instrumentation of the workplace and also the number of configuration parameters; therefore it enables a wide range of applications. Our aim is not only to signal risky situations caused by the misuse of the PPE (either due to its bad fitting or a wrong distance to the tool), but to intervene in a fast and robust way to avoid the safety risk. This solution is built upon previous results on the statistically sound measurement of distances and closeness in construction sites. Here, we contribute with a thorough analysis of collocating several BLE transmitters near orthogonally, which reduces interferences while avoiding the cost of more advanced technologies. We study how many transmitters are needed and what parameters are the best in the Bayesian filter for the optimal performance of the system. Real experiments with a prototype have been conducted in a construction workshop where a person operates a miter saw. The results show how the correct use of the PPE (an earmuff equipped with the BLE transmitters) can be inferred from the distance estimation in a robust and reliable way.This research received funding from Plan Propio-Universidad de Málaga and it is associated to the Proyecto Puente “Integración de dispositivos basados en el paradigma loT para la mejora de seguridad laboral en proyectos de contrucción (IoTcons)”. Funding for open access charge: Universidad de Málaga / CBU