相关论文
2017 - ICISA
Indoor Positioning Solely Based on User's Sight

Determination of the absolute geographical position has become every day routine, using the Global Positioning System (GPS), despite the prior existence of maps. However no equally universal solution has been developed for determining one’s location inside a building, which is an equally relevant problem statement, for which GPS cannot be used. Existing solutions usually involve additional infrastructure on the end of the location provider, such as beacon installations or particular configurations of wireless access points. These solutions are generally facilitated by additional native mobile applications on the client device, which connect to this infrastructure. We are aware of such solutions, but believe these to be lacking in simplicity. Our approach for indoor positioning alleviates the necessity for additional hardware by the provider, and software installation by the user. We propose to determine the user’s position inside a building using only a photo of the corridor visible to the user, uploading it to a local positioning server, accessible using a browser, which performs a classification of the photo based on a Neural Network approach. Our results prove the feasibility of our approach. One floor of the university’s building with partially very similar corridors has been learned by a deep convolutional neural network. A person lost in the building simply accesses the positioning server’s website and uploads a photo of his current line of sight. The server responds by generating and displaying a map of the building with the user’s current position and current direction.

2019 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Mobile Robot Localization with Reinforcement Learning Map Update Decision aided by an Absolute Indoor Positioning System

This paper introduces a new mobile robot localization solution consisting of two main modules: a Particle-Filter based Localization (PFL) and a Reinforcement-Learning based map updating, integrating relative measurements and absolute indoor positioning sensor (A-IPS) data. Concerning localization using 2D-LiDARs, featureless areas are known to be problematic. To solve this problem a classic PFL approach was modified to incorporate A-IPS position measurements in the prediction and update stages. The localization approach has the particularity of including the possibility of updating the map whenever major modifications are detected in the environment in relation to the current localization map. Due to the random sampling-based nature of the PFL, an associated map update solution is not trivial since small inconsistencies in the estimated pose can lead to erroneous map associations. The proposed method learns to decide by assigning higher rewards the greater is the overlap between the map and the 2DLIDAR scans, via RL, and then a proper update of the map is achieved. Validation of the proposed pipeline was carried out in a differential drive platform with algorithms developed in ROS. Tests were performed in two scenarios in order to assess the performance of both the localization module and the map update stage. The results show that the proposed localization method offers improvements in relation to known approaches, and consequently suggest promising perspectives for the proposed map update decision framework.

2018 - 2018 IEEE International Conference on Communications (ICC)
Radio Map Efficient Building Method Using Tensor Completion for WLAN Indoor Positioning System

Wireless local area network (WLAN) fingerprint-based indoor positioning system has a wide application prospect because it supplies good positioning performance without the requirement of additional hardware installations. However, huge labor and time are needed to establish the fingerprint database called radio map. To address this problem, we propose an efficient radio map building method using tensor completion. The cost of the proposed method is reduced by lessening the number of reference points (RPs) to be measured. Due to the strong correlation between data, estimating received signal strength (RSS) at unmeasured reference points can be formulated as a low rank tensor completion problem. And the issue can be transformed into a convex optimization problem by substituting rank operation with trace norm operation. We solve the problem by employing the alternating direction method of multipliers (ADMM) algorithm. The experiment results indicate that the proposed method can not only reduce the effort of radio map building remarkably, but also achieve high positioning accuracy.

2013
Design and Development of Indoor Positioning System: For Portable Devices

Indoor positioning has emerged in recent years for providing navigation information indoors and for other positioning related services. Indoor positioning and navigation in smart phones has become ...

2017 - 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB)
Impact and feasibility of darklight LED on indoor visible light positioning system

An unidirectional Visible Light Communication (VLC) can be used for an Indoor Positioning System (IPS). Mean-while, with our knowledge, most of VLC IPS are based on LED lights on, which cause energy waste when indoor lighting system is not necessary in the daytime. In this paper, we propose an IPS that uses DarkLight Visible Light Communication technique (DL-VLC). This proposal can guarantee the IPS function even when LED to be “OFF” and makes more economic energy. With our simulation results, the average of positioning error is 5 cm by considering the shot noise and thermal noise and the LED power of DarkVLC is 1.4 mW which is lower than that of LED in normal mode (19.8 W).

2015 - 2015 18th International Conference on Computer and Information Technology (ICCIT)
Extremely randomized trees for Wi-Fi fingerprint-based indoor positioning

Wi-Fi fingerprint-based position estimation becomes one of the key component of many location based services since the existing Wi-Fi infrastructures in indoor environment can be utilized for user's position estimation in order to reduce the deployment cost. However, the low positioning accuracy is still a key challenge for indoor positioning system due to the environmental dynamics and noisy characteristics of the RF signal. This paper presents a robust indoor localization approach based on Wi-Fi fingerprints using extremely randomized trees as the location estimation algorithm. In this approach, the collected raw fingerprints data are preprocessed, and then fed to the proposed localization algorithm, given their capability to handle high dimensional and unbalanced data, to localize users. The evaluation results of the experiments conducted on the first publicly available multi-building multi-floor indoor localization database indicate that the proposed technique performs much better than the traditional systems in terms of localization accuracy and calibration effort. The proposed approach yielded the maximum localization rate of 91.44%.

2015 - 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)
Preliminary investigation of indoor positioning system using Visible Light Communication

Indoor positioning research has been active for the past two decades. However no promising result has been identified for the position accuracy. Recently Light Emitting Diode (LED) lighting is used as a tool for the Visible Light Communication (VLC) research which opens a dimension in indoor positioning system (IPS). This paper presents a preliminary investigation of indoor positioning related to the accuracy of the position. In this proposed VLC-IPS, four LEDs are used as transmitters and a photodiode is used as receiver. The photodiode will obtain information from the transmitter to perform a coordinate estimation. A unique frequency modulation (F-ID) method is used on the transmitter to prevent interference; while on the receiver, Fast Fourier Transform (FFT) is used to retrieve the transmitted signal. In the meantime, the received signal strength (RSS) and trilateral methods are used to estimate the distance between the transmitters and receiver which then will estimate the coordinate of the receiver. Simulation results show that the average error between the exact position and the predicted position of the proposed system is 0.79 cm.

2012 - 2012 Third International Conference on Intelligent Systems Modelling and Simulation
Accuracy Enhancement of Fingerprint Indoor Positioning System

There are several researches have been conducted and different methods have been applied to extend the features of indoor location tracking using wireless devices. Fingerprint algorithm is one of the popular methods that have been used to track mobile nodes in indoor structure. This paper focuses on the enrichment of positioning accuracy of the Fingerprint algorithm. RSSI mechanism has been used on the Fingerprint Algorithm to encompass better performance for the accurate positioning. The paper has been proposed two different types of Fingerprint methods and their performance variants on different circumstances of indoor environment. The proposed Fingerprint Database mapping has been divided according to the RSSI data redundancy. The proposed Fingerprint methods has been successfully implemented and experimented on UTMMIMOS CoE lab. The experimented results of two Fingerprint methods has been analyzed and compared as well.

2017 - Sensors (Basel, Switzerland)
LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning

Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting solutions require extensive and error-free surveys of environments to build radio-map databases, which play a key role in position estimation. Fingerprinting also requires random updates of the database, when there are significant changes in the environment or a decrease in the accuracy. The calibration of the fingerprinting database is a time-consuming and laborious effort that prevents the extensive adoption of this technique. In this paper, we present a systematic LOCALIzation approach, “LOCALI”, for indoor positioning, which does not require a calibration database and extensive updates. The LOCALI exploits the floor plan/wall map of the environment to estimate the target position by generating radio maps by integrating path-losses over certain trajectories in complex indoor environments, where triangulation using time information or the received signal strength level is highly erroneous due to the fading effects caused by multi-path propagation or absorption by environmental elements or varying antenna alignment. Experimental results demonstrate that by using the map information and environmental parameters, a significant level of accuracy in indoor positioning can be achieved. Moreover, this process requires considerably lesser effort compared to the calibration-based techniques.

2020 - IEEE Internet of Things Journal
Kalman-Filter-Based Integration of IMU and UWB for High-Accuracy Indoor Positioning and Navigation

The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial measurement unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, ultrawideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under line-of-sight (LOS) conditions. Therefore, it is hard to use a standalone positioning and navigation system to achieve high accuracy in indoor environments. In this article, we propose an integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalman filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the dilution of precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of the least squares (LSs) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real-time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.

2018 - 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)
A smart phone based multi-floor indoor positioning system for occupancy detection

At present there is significant research interest in simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry; participants can carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi-floor indoor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple floors.

2016 - 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
A ToA/IMU indoor positioning system by extended Kalman filter, particle filter and MAP algorithms

This work introduces an indoor positioning system (IPS) which is a combination of wireless sensor network (WSN) and inertial navigation system (INS) for locating a moving object indoor. Here, WSN is adopted to measure the ranges from the unknown node to those anchor nodes by time of arrival (ToA) method. The core of the INS is the inertial measurement unit (IMU), which consists of accelerometers and gyroscopes. The real time inertial measurements from IMU and the range information by ToA method are both transmitted to processing terminal, where we propose to use three kinds of recursive Bayesian algorithms to make use of data to obtain the location estimations. The experimental results show that even with only two anchor nodes, the estimation accuracy of hybrid method by these three algorithms is higher than both standalone ToA method with 3 anchors and pure inertial solution.

2014 - International Journal of Software Engineering and its Applications
Wireless LAN/FM radio-based robust mobile indoor positioning: An initial outcome

The location determination in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site, especially within an indoor environment. Occasionally, this needs to integrate with other sensors in order to determine the location with greater intelligence, reliability and ubiquity. In this paper, we will utilise the function of a mobile sensing platform (MSP) by integrating a wireless local area network (WLAN) and FM radio. These positioning sensors will be switched based on the user’s environment in order to ensure the robustness of the indoor positioning system. Finally, we will present our preliminary results to illustrate the performance of the system for an indoor environment set-up.

2017 - Sensors (Basel, Switzerland)
An Improved Indoor Positioning System Using RGB-D Cameras and Wireless Networks for Use in Complex Environments

This work presents an Indoor Positioning System to estimate the location of people navigating in complex indoor environments. The developed technique combines WiFi Positioning Systems and depth maps, delivering promising results in complex inhabited environments, consisting of various connected rooms, where people are freely moving. This is a non-intrusive system in which personal information about subjects is not needed and, although RGB-D cameras are installed in the sensing area, users are only required to carry their smart-phones. In this article, the methods developed to combine the above-mentioned technologies and the experiments performed to test the system are detailed. The obtained results show a significant improvement in terms of accuracy and performance with respect to previous WiFi-based solutions as well as an extension in the range of operation.

2010 - Int. J. Handheld Comput. Res.
Indoor Positioning Using FM Radio

This paper presents an indoor positioning system based on FM radio. The system is built on commercially available short-range FM transmitters. This is the first experimental study of FM performance for indoor localisation. FM radio possesses a number of features, which make it distinct from other localisation technologies. Despite the low cost and off-the-shelf components, this FM positioning system reaches a high performance, comparable to other positioning technologies such as Wi-Fi. The authors' experiments have yielded a median accuracy of 1.0 m and in 95% of cases the error is below 5 m.

2017 - 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)
Improved VLC-based indoor positioning system using a regression approach with conventional RSS techniques

There is a recent interest to develop an indoor positioning system using visible light communications technology. Based on received signal strength (RSS), this paper combines a simple regression-based approach with linear and nonlinear least square estimations (LLS & NLS). The performance of regression approach is evaluated by different metrics such as the average, standard deviation, and cumulative distribution function of the localization error. In the system analysis, first reflection from plaster walls (high reflectivity 0.8), thermal noise, and shot noise are considered. The simulation results show that almost all the room (99.4%) has error less than 0.6 m by using regression approach instead of (72.19%) by using the classical approaches. The maximum error decreases from 1.15 m to 0.73 m, the average error from 0.37 m to 0.22 m, and the standard deviation from 0.28 m to 0.15 m. Thus, maximum error is improved by (36.5%), average error by (41%), and standard deviation by (46%). A key conclusion of this work is that, using the regression method enhances the performance of VLC indoor positioning system.

2012
A WLAN Fingerprinting Based Indoor Localization Technique

Satellite-based Global Positioning Systems (GPS) have enabled a variety of location-based services such as navigation systems, and become increasingly popular and important in our everyday life. However, GPS does not work well in indoor environments where walls, floors and other construction objects greatly attenuate satellite signals. In this paper, we propose an Indoor Positioning System (IPS) based on widely deployed indoor WiFi systems. Our system uses not only the Received Signal Strength (RSS) values measured at the current location but also the previous location information to determine the current location of a mobile user. We have conducted a large number of experiments in the Schorr Center of the University of Nebraska-Lincoln, and our experiment results show that our proposed system outperforms all other WiFi-based RSS IPSs in the comparison, and is 5% more accurate on average than others. iii ACKNOWLEDGMENTS Firstly, I would like to express my heartfelt gratitude to my advisor and committee chair, Professor Lisong Xu and the co-advisor Professor Zhigang Shen for their constant encouragement and guidance throughout the course of my master's study and all the stages of the writing of this thesis. Without their consistent and illuminating instruction, this thesis work could not have reached its present form. Their technical and editorial advice and infinite patience were essential for the completion of this thesis. I feel privileged to have had the opportunity to study under them. I thank Professor Ziguo Zhong and Professor Mehmet Vuran for serving on my Master's Thesis defense committee, and their involvement has greatly improved and clarified this work. I specially thank Prof Ziguo Zhong again, since his support has always been very generous in both time and research resources. I thank all the CSE staff and friends, for their friendship and for all the memorable times in UNL. I would like to thank everyone who has helped me along the way. At last, I give my deepest thanks go to my parents for their self-giving love and support throughout my life.

2014 - Electronics Letters
Highly accurate 3D wireless indoor positioning system using white LED lights

A wireless indoor positioning system using white LED lights is proposed. The time difference of arrival technique is employed and the phase differences between the received signals are determined to develop a positioning algorithm which can estimate the receiver location with a mean localisation error as low as 1 mm in a room of dimensions 5 × 5 × 3 m. Through simulations, it is identified that the optimum receiver height where localisation error gets minimised is between 2.5 and 3 m from the ceiling which corresponds well with the typical dimensions of a room.

2005 - 2005 International Conference on Wireless Networks, Communications and Mobile Computing
Efficient design of indoor positioning systems based on location fingerprinting

This paper presents results of systematic study to improve designing of indoor positioning systems based on location fingerprinting technique. Thus far, researchers have been focusing on improving this type of positioning systems by arbitrarily selecting one of available pattern classification techniques. On the contrary, this study suggests a modeling framework which can be used as a tool to efficiently design such systems. Using the proposed framework, this paper quantifies accuracy and precision improvement resulted from adjusting system parameters. Consequentially, deployment and performance tuning time which is the most time-consuming process of the fingerprinting technique can be reduced. A system model based on proposed framework is validated with a prototype in which both model and prototype utilize simple Euclidean distance as a technique for location determination.

2015 - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
A mobile indoor positioning system based on iBeacon technology

To increase the efficiency in the emergency room, the goal of this research is to implement a mobile-based indoor positioning system using mobile applications (APP) with the iBeacon solution based on the Bluetooth Low Energy (BLE) technology. We use the Received Signal Strength (RSS) based localization method to estimate the patients' locations. Our positioning algorithm achieves 97.22% (95% Confidence Interval = 95.90% - 98.55%) accuracy of classification. As the result, our mechanism is reliable enough to satisfy the need for medical staff to track the locations of their patients.

A survey of indoor positioning systems for wireless personal networks

Ignas Niemegeers
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Yanying Gu
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Anthony Lo
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I. Niemegeers
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A. Lo
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Yanying Gu

Abstract:Recently, indoor positioning systems (IPSs) have been designed to provide location information of persons and devices. The position information enables location-based protocols for user applications. Personal networks (PNs) are designed to meet the users' needs and interconnect users' devices equipped with different communications technologies in various places to form one network. Location-aware services need to be developed in PNs to offer flexible and adaptive personal services and improve the quality of lives. This paper gives a comprehensive survey of numerous IPSs, which include both commercial products and research-oriented solutions. Evaluation criteria are proposed for assessing these systems, namely security and privacy, cost, performance, robustness, complexity, user preferences, commercial availability, and limitations.We compare the existing IPSs and outline the trade-offs among these systems from the viewpoint of a user in a PN.

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引用
Analysis of Wi-Fi Signal Characteristics for Indoor Positioning Measurement
2012
입체적 지형을 고려한 무인항공기의 실시간 촬영 영역 분석 알고리즘
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Real-Time Shooting Area Analysis Algorithm of UAV Considering Three-Dimensional Topography
2013
스마트폰 IMU와 WPS를 결합한 복합 측위 방법론
2013
IEEE 802.11 기반 저전력 위치 추적 장치의 설계 및 구현
2014
DarkDroid - Exposing the Dark Side of Malicious Mobile Applications
ERCIM News
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OSEM : occupant-specific energy monitoring.
2016
About Location-aware Mobile Messages: Expert System Based on WiFi Spots
2011 Fifth International Conference on Next Generation Mobile Applications, Services and Technologies
2011
Indoor navigation algorithm for mobile robot using wireless sensor networks
2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE)
2017
ISRAA S. AL-FURATI et al: IR SENSORS ARRAY FOR ROBOTS LOCALIZATION USING K MEANS CLUSTERING
2019
A Systematic Literature Review of Indoor Position System Accuracy and Implementation
2018 International Conference on Applied Science and Technology (iCAST)
2018
Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part I
AIAI
2020
Performance Analysis of Zigbee Mesh Topology by Varying Trajectory
2016
Static beacons based indoor positioning method for improving room-level accuracy
2015
Intelligent Software Methodologies, Tools and Techniques
Communications in Computer and Information Science
2014
A Self-Adaptive AP Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning
IEEE Internet of Things Journal
2021
Pedestrian Navigation Services: Challenges and Current Trends
2013
Channel state information–based multi-level fingerprinting for indoor localization with deep learning
Int. J. Distributed Sens. Networks
2018
A Novel Method for Constructing a WIFI Positioning System with Efficient Manpower
Sensors
2015
Towards Mobile Information Systems for Indoor Space
Mob. Inf. Syst.
2016