Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons

Ciabattoni, Lucio; Foresi, G.; Monteriù, Andrea; Pepa, Lucia; Pagnotta, D. Proietti; Spalazzi, Luca; Verdini, Federica

doi:10.1007/s12652-017-0579-0

Cited by 54 publications

(30 citation statements)

References 33 publications

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“…It has the advantages of low transmission power, high performance, and easy deployment, and has become a widely used positioning technology. The positioning accuracy is generally 3-5 m [7,8]. Pseudolite indoor positioning technology is a mainstream solution to seamless indoor and outdoor positioning and navigation.…”

Section: Introductionmentioning

confidence: 99%

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

Huang

et al. 2020

Sensors

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In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

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Section: Introductionmentioning

confidence: 99%

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

Huang

et al. 2020

Sensors

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“…New methods range from pervasive sensors found in everyday devices to fast communication and sophisticated AI algorithms able to aggregate multiple streams of data and recognise patterns. In an emergency context, the Internet of Things (IoT) is thought to have a positive impact (Yang et al, 2013), whether during preliminary evaluation of an incident, critical to a plan-based approach (Chen et al, 2008), or during the course of an event to track and monitor people's actions, locations, and considerable other information (Scheurer et al, 2017;O'Flynn et al, 2018;Ciabattoni et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Toward the Self-Organisation of Emergency Response Teams Based on Morphogenetic Network Growth

Toussaint

Norling

Doursat

2019

The 2019 Conference on Artificial Life

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Focusing on the challenge of fostering the self-assembly of socio-technical networks, we present the application of Morphogenetic Engineering principles in this domain to a 2D spatial case study involving a team of first responders. Our model and simulation illustrate how members of a rescue team could be guided via hand-held devices toward better coordination and positioning at appropriate locations, based on peer-to-peer communication and local landmarks in the environment (such as incidents or exits), without the need for a centralised control centre. Using Raspberry Pi devices, we illustrate this scenario in various situations that require quick decision-making to control and manage. Our work suggests the possibility of novel forms of bottom-up self-organisation among groups of users and machines, in contrast to top-down imposed hierarchies and policies.

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“…Tag-based approach: In this approach, targets carry a tag as a unique ID, which uses different communication technologies, such as radio-frequency identification (RFID) [ 11 , 12 , 13 ], Bluetooth [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ], ZigBee, and Wi-Fi [ 21 , 22 , 23 , 24 ], to broadcast their unique ID in a specific time interval. Additionally, there are scanners allocated in different areas that listen to the broadcasting packets to localize the tag and the individual.…”

Section: Introductionmentioning

confidence: 99%

“…It is recommended by the authors that to ease the localization, appropriate filtering or data processing after the initial distance estimation should be carried out. The researchers in [ 16 ] propose a hybrid real-time indoor localization architecture, based on smartphone and BLE beacons exploiting the complementary characteristics of the proximity (RSS method) and pedestrian dead-reckoning (PDR) approaches. Likewise, the authors of [ 17 ] report the design, implementation, and evaluation of an Internet of Things (IoT)-based system for indoor localization using BLE technology.…”

Section: Introductionmentioning

confidence: 99%

Multi-Residential Activity Labelling in Smart Homes with Wearable Tags Using BLE Technology

Mokhtari

Anvari‐Moghaddam

Zhang

et al. 2018

Sensors

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Smart home platforms show promising outcomes to provide a better quality of life for residents in their homes. One of the main challenges that exists with these platforms in multi-residential houses is activity labeling. As most of the activity sensors do not provide any information regarding the identity of the person who triggers them, it is difficult to label the sensor events in multi-residential smart homes. To deal with this challenge, individual localization in different areas can be a promising solution. The localization information can be used to automatically label the activity sensor data to individuals. Bluetooth low energy (BLE) is a promising technology for this application due to how easy it is to implement and its low energy footprint. In this approach, individuals wear a tag that broadcasts its unique identity (ID) in certain time intervals, while fixed scanners listen to the broadcasting packet to localize the tag and the individual. However, the localization accuracy of this method depends greatly on different settings of broadcasting signal strength, and the time interval of BLE tags. To achieve the best localization accuracy, this paper studies the impacts of different advertising time intervals and power levels, and proposes an efficient and applicable algorithm to select optimal value settings of BLE sensors. Moreover, it proposes an automatic activity labeling method, through integrating BLE localization information and ambient sensor data. The applicability and effectiveness of the proposed structure is also demonstrated in a real multi-resident smart home scenario.

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Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons

Cited by 54 publications

References 33 publications

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

Toward the Self-Organisation of Emergency Response Teams Based on Morphogenetic Network Growth

Multi-Residential Activity Labelling in Smart Homes with Wearable Tags Using BLE Technology

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