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Indoor Positioning: Comparing Different Techniques and Choosing the Best One for a User Authentication Real Scenario
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The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones‐based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real‐world applications. A taxonomy of smartphone sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting, and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients.
Indoor positioning methods using radio networks are investigated. Time Difference of Arrival (TDOA) method is studied deeply, and the main problems are revealed. Application of ultra-wide band (UWB) radio technology to TDOA method is discussed, and limitations to UWB receiver and transmitter are revealed. These results are of great importance for the organization of unmanned moving devices management in the paradigm of fully autonomous Fabric of the Future in Industry 4.0.
In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been pre-recorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy.
Indoor location positioning techniques have experienced a significant growth in recent years. This work presents a hybrid indoor positioning system with fine and coarse modes. It utilises acoustic signals for fine positioning and received signal strength (RSS) for coarse location estimation. Acoustic positioning systems require a line‐of‐sight connection for accurate positioning which may not be available due to obstacles in indoor environments. A new solution is presented to overcome this problem using RSS as a reference to validate the line‐of‐sight connection. Moreover, a new digital signal processing algorithm using a matched filter is presented to enhance the system's robustness in indoor environments with low a signal‐to‐noise ratio. Experimental measurement results in an indoor environment show that the proposed solution can accurately determine indoor locations with <6 cm positioning error on average.
Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.
In the indoor environment, variation of the magnetic field is caused by building structures, and magnetic field map navigation is based on this feature. In order to estimate position using this navigation, a three-axis magnetic field must be measured at every point to build a magnetic field map. After the magnetic field map is obtained, the position of the mobile robot can be estimated with a likelihood function whereby the measured magnetic field data and the magnetic field map are used. However, if only magnetic field map navigation is used, the estimated position can have large errors. In order to improve performance, we propose a particle filter system that integrates magnetic field map navigation and an encoder system. In this paper, multiple magnetic sensors and three magnetic field maps (a horizontal intensity map, a vertical intensity map, and a direction information map) are used to update the weights of particles. As a result, the proposed system estimates the position and orientation of a mobile robot more accurately than previous systems. Also, when the number of magnetic sensors increases, this paper shows that system performance improves. Finally, experiment results are shown from the proposed system that was implemented and evaluated.
We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.
Angle of arrival (AOA) has previously been used for outdoor positioning in aircraft navigation and for services like E911. For indoor positioning, the best schemes to date rely either on extensive infrastructure, or on sampling of the signal strength on a dense grid, which is subject to changes in the environment, like furniture, elevators, or people. We present an indoor positioning architecture that does not require a signal strength map, simply requiring the placement of special VOR base stations (VORBA). While our incipient realization of the AOA using 802.11 uses a base station with a revolving directional antenna, a non mechanical implementation would yield comparable performance, even with quantized angles. Performance of positioning with VOR base stations is evaluated though experimentation, simulation, and theoretical analysis.
With the rapid development of Internet of Things (IoT) technology, location-based services have been widely applied in the construction of smart cities. Satellite-based location services have been utilized in outdoor environments, but they are not suitable for indoor technology due to the absence of global positioning system (GPS) signal. Therefore, many indoor localization technologies and systems have emerged by utilizing many other signals. In particular, fingerprinting localization has recently garnered attention because its promising performance. In this work, we aim to study recent indoor localization technologies and systems based on various fingerprints, which use machine learning and intelligent algorithms. We also present the architecture of intelligent localization. The development of indoor localization technology should have the ability of self-adaptation and self-learning in the future. And the architecture shows how to make localization become more “smart” by advanced techniques. The state-of-the-art localization systems’ working principles are summarized and compared in terms of their localization accuracy, latency, energy consumption, complexity, and robustness. We also discuss the challenges of existing indoor localization technologies, potential solutions to these challenges, and possible improvement measures.
Collecting data like location information is an essential part of concepts like the “IoT” or “Industry 4.0”. In the case of the development of a precise localization system and an integrated navigation system, indoor location technology receives more and more attention and has become a hot research topic. Common indoor location techniques are mainly based on wireless local area network, radio frequency tag, ZigBee technology, Bluetooth technology, infrared technology and ultra-wideband (UWB). However, these techniques are vulnerable to various noise signals and indoor environments, and also the positioning accuracy is easily affected by the complicated indoor environment. We studied the problem of real-time location tracking based on UWB in an indoor environment in this paper. We have proposed a combinational filtering algorithm and an improved Two-Way Ranging (ITWR) method for indoor real-time location tracking. The simulation results prove that the real-time performance and high accuracy of the presented algorithm can improve location accuracy. The experiment shows that the combinational algorithm and ITWR method which are applied to the positioning and navigation of the smart supermarket, have achieved quiet good results in positioning accuracy. The average positioning error is less than 10[Formula: see text]cm, some of the improvements can elevate the positioning accuracy by 17.5%. UWB is a suitable method for indoor real-time location tracking and has important theoretic value and practical significance.
The proliferation of mobile computing devices and local-area
wireless networks has fostered a growing interest in location-aware
systems and services. In this paper we present RADAR, a radio-frequency
(RF)-based system for locating and tracking users inside buildings.
RADAR operates by recording and processing signal strength information
at multiple base stations positioned to provide overlapping coverage in
the area of interest. It combines empirical measurements with signal
propagation modeling to determine user location and thereby enable
location-aware services and applications. We present experimental
results that demonstrate the ability of RADAR to estimate user location
with a high degree of accuracy
A hybrid indoor positioning system for blind and visually impaired using Bluetooth and Google tango
- V Nair
- C Tsangouri
- B Xiao
- G Olmschenk
- W H Seiple
- Z Zhu
Spotfi: Decimeter level localization using wifi
- M Kotaru
- K Joshi
- D Bharadia
- S Katti