A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.

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Indoor Localization System Using Wireless Sensor Network

Iraqi Journal of Computer Communication Control and System Engineering ◽

10.33103/uot.ijccce.18.1.3 ◽

2018 ◽

pp. 29-38

Keyword(s):

Indoor Localization ◽

Low Cost ◽

Wide Band ◽

Human Movement ◽

Indoor Positioning ◽

Positioning System ◽

Wireless System ◽

Anchor Nodes ◽

Indoor Positioning System ◽

Better Than

Building a precise low cost indoor positioning and navigation wireless system is a challenging task. The accuracy and cost should be taken together into account. Especially, when we need a system to be built in a harsh environment. In recent years, several researches have been implemented to build different indoor positioning system (IPS) types for human movement using wireless commercial sensors. The aim of this paper is to prove that it is not always the case that having a larger number of anchor nodes will increase the accuracy. Two and three anchor nodes of ultra-wide band with or without the commercial devices (DW 1000) could be implemented in this work to find the Localization of objects in different indoor positioning system, for which the results showed that sometimes three anchor nodes are better than two and vice versa. It depends on how to install the anchor nodes in an appropriate scenario that may allow utilizing a smaller number of anchors while maintaining the required accuracy and cost.

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THE DESIGN AND IMPLEMENTATION OF INDOOR LOCALIZATION SYSTEM USING MAGNETIC FIELD BASED ON SMARTPHONE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-379-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 379-383

Author(s):

J. Liu ◽

C. Jiang ◽

Z. Shi

Keyword(s):

Magnetic Field ◽

Indoor Localization ◽

Indoor Positioning ◽

Positioning System ◽

Localization Algorithm ◽

External Device ◽

Localization System ◽

Filtering Algorithm ◽

Coordinate Conversion ◽

Indoor Positioning System

Sufficient signal nodes are mostly required to implement indoor localization in mainstream research. Magnetic field take advantage of high precision, stable and reliability, and the reception of magnetic field signals is reliable and uncomplicated, it could be realized by geomagnetic sensor on smartphone, without external device. After the study of indoor positioning technologies, choose the geomagnetic field data as fingerprints to design an indoor localization system based on smartphone. A localization algorithm that appropriate geomagnetic matching is designed, and present filtering algorithm and algorithm for coordinate conversion. With the implement of plot geomagnetic fingerprints, the indoor positioning of smartphone without depending on external devices can be achieved. Finally, an indoor positioning system which is based on Android platform is successfully designed, through the experiments, proved the capability and effectiveness of indoor localization algorithm.

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Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Entropy ◽

10.3390/e23111401 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1401

Author(s):

Haq Nawaz ◽

Ahsen Tahir ◽

Nauman Ahmed ◽

Ubaid U. Fayyaz ◽

Tayyeb Mahmood ◽

...

Keyword(s):

Low Power ◽

Indoor Localization ◽

Low Cost ◽

Smart Homes ◽

Indoor Positioning ◽

High Accuracy ◽

Positioning System ◽

Telemetry Data ◽

Ultra Low Power ◽

Indoor Positioning System

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

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Design of a Bell-Shaped Ultra Wideband Antenna for Indoor Localization System

International Journal on Communications Antenna and Propagation (IRECAP) ◽

10.15866/irecap.v5i6.7666 ◽

2015 ◽

Vol 5 (6) ◽

pp. 323 ◽

Cited By ~ 1

Author(s):

Nadia Ghariani ◽

Mohamed Salah Karoui ◽

Mondher Chaoui ◽

Mongi Lahiani ◽

Hamadi Ghariani

Keyword(s):

Indoor Localization ◽

Ultra Wideband ◽

Localization System ◽

Wideband Antenna

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Indoor Positioning Method Based on Infrared Vision and UWB Fusion

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012070 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012070

Author(s):

Qianrong Zhang ◽

Yi Li

Keyword(s):

Real Time ◽

Indoor Localization ◽

Low Cost ◽

Ultra Wideband ◽

Coverage Area ◽

Positioning Accuracy ◽

Mobile Vehicle ◽

Positioning Method ◽

Positioning Algorithm ◽

Infrared Vision

Abstract Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.

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Development of a low-cost reliable indoor localization system targeted to older users with mild cognitive impairments

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems ◽

10.2991/racs-15.2016.25 ◽

2016 ◽

Author(s):

Carlos Velasco ◽

Yehya Mohamad ◽

Alan John

Keyword(s):

Indoor Localization ◽

Low Cost ◽

Cognitive Impairments ◽

Localization System ◽

Older Users

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Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

Journal of Sensors ◽

10.1155/2017/9742170 ◽

2017 ◽

Vol 2017 ◽

pp. 1-16 ◽

Cited By ~ 27

Author(s):

Santosh Subedi ◽

Jae-Young Pyun

Keyword(s):

Estimation Error ◽

Indoor Positioning ◽

Location Estimation ◽

Reference Points ◽

Ultra Wideband ◽

Positioning System ◽

Localization Method ◽

Weighted Centroid Localization ◽

Time Required ◽

Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

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Mobile Robot Indoor Positioning System Based on K-ELM

Journal of Sensors ◽

10.1155/2019/7547648 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Haixia Wang ◽

Junliang Li ◽

Wei Cui ◽

Xiao Lu ◽

Zhiguo Zhang ◽

...

Keyword(s):

Mobile Robot ◽

Indoor Localization ◽

Indoor Positioning ◽

Positioning System ◽

Positioning Accuracy ◽

Positioning Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Positioning Algorithm ◽

Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

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Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Sensors ◽

10.3390/s19173657 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3657 ◽

Cited By ~ 1

Author(s):

Michał R. Nowicki ◽

Piotr Skrzypczyński

Keyword(s):

Indoor Localization ◽

Dead Reckoning ◽

Indoor Positioning ◽

Positioning System ◽

Indoor Environments ◽

University Campus ◽

Sources Of Information ◽

Visual Place Recognition ◽

Indoor Positioning System

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

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