Survey on Wireless Indoor Positioning Systems

Indoor positioning has finally testified a rise in interest, thanks to the big selection of services it is provided, and ubiquitous connectivity. There are currently many systems that can locate a person, be it wireless or by mobile phone and the most common systems in outdoor environments is the GPS, the most common in indoor environments is Wi-Fi positioning technique positioning. The improvement of positioning systems in indoor environments is desirable in many areas as it provides important facilities and services, such as airports, universities, factories, hospitals, and shopping malls. This paper provides an overview of the existing methods based on wireless indoor positioning technique. We focus in this survey on the strengths of these systems mentioned in the literature discordant with the present surveys; we also assess to additionally measure various systems from the scene of energy efficiency, price, and following accuracy instead of comparing the technologies, we also to additionally discuss residual challenges to correct indoor positioning

Indoor positioning system survey using BLE beacons

This project provides a survey of indoor positioning systems and reports experimental work with Bluetooth Low Energy (BLE) Beacons. A positioning algorithm based on the Received Signal Strength Index (RSSI) from Bluetooth Low Energy signals is proposed for indoor tracking of the position of a drone. Experimental tests for characterization of beacon signals are presented. The application of a Kalman filter to reduce the effect of fluctuations in beacons signals is described

RSSI based self-adaptive algorithms targeting indoor localisation under complex non-line of sight environments

Location Based Services (LBS) are a relatively recent multidisciplinary field which brings together many aspects of the fields of hardware design, digital signal processing (DSP), digital image processing (DIP), algorithm design in mathematics, and systematic implementation. LBS provide indirect location information from a variety of sensors and present these in an understandable and intuitive way to users by employing theories of data science and deep learning. Indoor positioning, which is one of the sub-applications of LBS, has become increasingly important with the development of sensor techniques and smart algorithms. The aim of this thesis is to explore the utilisation of indoor positioning algorithms under complex Non-Line of sight (LOS) environments in order to meet the requirements of both commercial and civil indoor localisation services. This thesis presents specific designs and implementations of solutions for indoor positioning systems from signal processing to positioning algorithms. Recently, with the advent of the protocol for the Bluetooth 4.0 technique, which is also called Bluetooth Low Energy (BLE), researchers have increasingly begun to focus on developing received signal strength (RSS) based indoor localisation systems, as BLE based indoor positioning systems boast the advantages of lower cost and easier deployment condition. At the meantime, information providers of indoor positioning systems are not limited by RSS based sensors. Accelerometer and magnetic field sensors may also being applied for providing positioning information by referring to the users’ motion and orientation. With regards to this, both indoor localisation accuracy and positioning system stability can be increased by using hybrid positioning information sources in which these sensors are utilised in tandem. Whereas both RSS based sensors, such as BLE sensors, and other positioning information providers are limited by the fact that positioning information cannot be observed or acquired directly, which can be summarised into the Hidden Markov Mode (HMM). This work conducts a basic survey of indoor positioning systems, which include localisation platforms, using different hardware and different positioning algorithms based on these positioning platforms. By comparing the advantages of different hardware platforms and their corresponding algorithms, a Received Signal Strength Indicator (RSSI) based positioning technique using BLE is selected as the main carrier of the proposed positioning systems in this research. The transmission characteristics of BLE signals are then introduced, and the basic theory of indoor transmission modes is detailed. Two filters, the smooth filter and the wavelet filter are utilised to de-noise the RSSI sequence in order to increase localisation accuracy. The theory behind these two filter types is introduced, and a set of experiments are conducted to compare the performance of these filters. The utilisation of two positioning systems is then introduced. A novel, off-set centroid core localisation algorithm is proposed firstly and the second one is a modified Monte Carlo localisation (MCL) algorithm based system. The first positioning algorithm utilises BLE as a positioning information provider and is implemented with a weighted framework for increasing localisation accuracy and system stability. The MCL algorithm is tailor-made in order to locate users’ position in an indoor environment using BLE and data received by sensors locating user position in an indoor environment. The key features in these systems are summarised in the following: the capacity of BLE to compute user position and achieve good adaptability in different environmental conditions, and the compatibility of implementing different information sources into these systems is very high. The contributions of this thesis are as follows: Two different filters were tailor-made for de-nosing the RSSI sequence. By applying these two filters, the localisation error caused by small scale fading is reduced significantly. In addition, the implementation for the two proposed are described. By using the proposed centroid core positioning algorithm in combination with a weighted framework, localisation inaccuracy is no greater than 5 metres under most complex indoor environmental conditions. Furthermore, MCL is modified and tailored for use with BLE and other sensor readings in order to compute user positioning in complex indoor environments. By using sensor readings from BLE beacons and other sensors, the stability and accuracy of the MCL based indoor position system is increased further

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

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform

With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations