scholarly journals A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

2019 ◽  
Vol 11 (5) ◽  
pp. 504 ◽  
Author(s):  
Yue Yu ◽  
Ruizhi Chen ◽  
Liang Chen ◽  
Guangyi Guo ◽  
Feng Ye ◽  
...  
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Line Of Sight ◽  
Multiple Sensors ◽  
Ranging Model

More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

scholarly journals Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

2020 ◽  
Vol 10 (6) ◽  
pp. 2003 ◽  
Author(s):  
Liu Liu ◽  
Bofeng Li ◽  
Ling Yang ◽  
Tianxia Liu
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Reasonable Accuracy ◽  
Positioning Accuracy ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Smartphone Sensors ◽  
Pedestrian Dead Reckoning

For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.

scholarly journals Indoor Positioning Method Based on Infrared Vision and UWB Fusion

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.

scholarly journals Indoor Localization System Using Wireless Sensor Network

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.

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

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3657 ◽  
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.

scholarly journals An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4550 ◽  
Author(s):  
Vasilis Stavrou ◽  
Cleopatra Bardaki ◽  
Dimitris Papakyriakopoulos ◽  
Katerina Pramatari
Keyword(s):  
Random Forest ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Retail Store ◽  
Low Energy ◽  
Location Based Services ◽  
Shopping Behavior ◽  
Multiple Sources ◽  
Bluetooth Low Energy ◽  
Product Categories

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

An Intelligent Navigation Solution for Land Mobile Location-Based Services

2002 ◽  
Vol 55 (2) ◽  
pp. 225-240 ◽  
Author(s):  
Stephen Scott-Young ◽  
Allison Kealy
Keyword(s):  
Firm Growth ◽  
Spatial Information ◽  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Geographical Information ◽  
Navigation Systems ◽  
Navigation Solution ◽  
Lower Accuracy

The increasing availability of small, low-cost GPS receivers has established a firm growth in the production of Location-Based Services (LBS). LBS, such as in-car navigation systems, are not necessarily reliant on high accuracy but a continuous positioning service. When available, the accuracy provided by the standard positioning service (SPS) of 30 metres, 95% of the time is often acceptable. The reality is, however, that GPS does not work in all situations, and it is therefore common to integrate GPS with additional sensors. The use of low-cost inertial sensors alone during GPS signal outage is severely restricted due to the accumulation of errors that is inherent with such dead reckoning (DR) systems. Through the integration of spatial information with real-time positioning sensors, intelligence can be added to the land mobile navigation solution. The information contained within a Geographical Information System (GIS) provides additional observations that can be used to improve the navigation result. With this approach, the solution is not dependent on the performance capabilities of the navigation sensors alone. This enables the use of lower accuracy navigation devices, allowing low-cost systems to provide a sustained, viable navigation solution despite long-term GPS outages. Practical results are presented comparing solutions obtained from a hand-held GPS receiver to a gyroscope and odometer.

PDR-Aided Algorithm with WiFi Fingerprint Matching for Indoor Localization

2014 ◽  
Vol 701-702 ◽  
pp. 989-993
Author(s):  
Wen Bin Yu ◽  
Peng Li ◽  
Zhi Chen ◽  
Chang Li
Keyword(s):  
Social Network ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Fingerprint Matching ◽  
Network Applications ◽  
Wireless Signal ◽  
Signal Fluctuation ◽  
Pedestrian Dead Reckoning ◽  
Wifi Fingerprint

Recently, indoor localization is essential to enable location-based services for many mobile and social network applications. Due to fluctuation of the wireless signal, the accuracy of a simple WiFi fingerprint-based localization is not high. In this paper, we first exploit Pedestrian Dead Reckoning (PDR) technology to overcome the problem of the wireless signal fluctuation, then propose a PDR-aided algorithm with WiFi fingerprint matching for indoor localization, which using the PDR technology aided indoor localization. Experiments show that our algorithm has better accuracy than other indoor localization methods.

scholarly journals A REVIEW ON INDOOR LOCALIZATION WITH INTERNET OF THINGS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 121-128
Author(s):  
C. Basri ◽  
A. Elkhadimi
Keyword(s):  
Internet Of Things ◽  
Network Architecture ◽  
Indoor Localization ◽  
Smart Cities ◽  
Indoor Positioning ◽  
Machine Learning Algorithms ◽  
Location Based Services ◽  
Positioning System ◽  
Resources Management ◽  
Indoor Positioning System

Abstract. The advancement of Internet of things (IoT) has revolutionized the field of telecommunication opening the door for interesting applications such as smart cities, resources management, logistics and transportation, wearables and connected healthcare. The emergence of IoT in multiple sectors has enabled the requirement for an accurate real time location information. Location-based services are actually, due to development of networks, sensors, wireless communications and machine learning algorithms, able to collect and transmit data in order to determine the target positions, and support the needs imposed by several applications and use cases. The performance of an indoor positioning system in IoT networks depends on the technical implementation, network architecture, the deployed technology, techniques and algorithms of positioning. This paper highlights the importance of indoor localization in internet of things applications, gives a comprehensive review of indoor positioning techniques and methods implemented in IoT networks, and provides a detailed analysis on recent advances in this field.

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