A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications

Correa, Alejandro; Barceló, Marc; Morell, Antoni; Vicario, José López

doi:10.3390/s17081927

Cited by 81 publications

(37 citation statements)

References 80 publications

(134 reference statements)

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“…A standard log-distance path loss model has been selected to convert RSSI to distance as shown in Equation (1). Usually parameter d 0 is fixed to 1 meter, and RSSI(d 0 ) is the average measured RSSI when the receiver is 1 meter away from the transmitter as is required in all logarithmic models.…”

Section: Distance Model and Position Estimationmentioning

confidence: 99%

“…Radio-based indoor positioning, including BLE positioning, generally can be divided into three broad categories: proximity, Time of Flight (TOF) measurements, and Received Signal Strength Indicator (RSSI) based methods. In some cases these have also been augmented with Angle of Arrival (AOA) [1]. RSSI-based ranging and fingerprinting are two common methods that rely on the RSSI measurements for positioning.…”

Section: Introductionmentioning

confidence: 99%

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Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Naghdi

O’Keefe

2020

Sensors

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One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

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Section: Distance Model and Position Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Naghdi

O’Keefe

2020

Sensors

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“…The continuous improvement of the performance of hardware sensor equipment and the ultrahigh penetration rate of smart phones lay the foundation for positioning human indoor activity patterns [4]. These technologies provide strong hardware support for understanding, forecasting, and analyzing indoor human activities.…”

Section: Introductionmentioning

confidence: 99%

A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning

Wang

2019

IJGI

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The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future.

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“…However, there is a great deal of ongoing work in this area. For example, there are standards for connecting to and synchronizing second screen devices [45]; methods for synchronizing audio, video, and data over Wi-Fi [46]; toolboxes for creating ad hoc networks of mobile devices for musical performance [47]; and various indoor positioning systems (for people and objects) utilizing different technologies [48]. Implementation of technical solutions will be the focus of future work.…”

Section: Technicalmentioning

confidence: 99%

Qualitative Evaluation of Media Device Orchestration for Immersive Spatial Audio Reproduction

Francombe¹,

Woodcock²,

Hughes³

et al. 2018

J. Audio Eng. Soc.

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A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications

Cited by 81 publications

References 80 publications

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning

Qualitative Evaluation of Media Device Orchestration for Immersive Spatial Audio Reproduction

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