The iLoc+ Ultrasound Indoor Localization System for AAL Applications at EvAAL 2012

Knauth, Stefan; Andrushevich, Aliaksei; Kaufmann, Lukas; Kistler, Rolf; Klapproth, Alexander

doi:10.1007/978-3-642-37419-7_7

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Current indoor tracking tracking techniques [20] [21] fall into two categories: i) wireless and ii) vision. Wireless technologies include GPS [22] [23], Ultrasound [24] [25], RFID [26] [27], WLAN [28] [29] and Bluetooth [30] [31], while vision technologies include fixed and mobile camera based tracking. Wireless technologies are characterized by a transmitter that sends a radio signal and a receiver on the other end.…”

Section: B Current Indoor Tracking Methodsmentioning

confidence: 99%

Privacy Concerns Regarding Occupant Tracking in Smart Buildings

Kessler,

Masiane,

Abdelhalim

2020

Preprint

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Tracking of occupants within buildings has become a topic of interest in the past decade. Occupant tracking has been used in the public safety, energy conservation, and marketing fields. Various methods have been demonstrated which can track people outside of and inside buildings; including GPS, visual-based tracking using surveillance cameras, and vibration based tracking using sensors such as accelerometers. In this work, those main systems for tracking occupants are compared and contrasted for the levels of detail they give about where occupants are, as well as their respective privacy concerns and how identifiable the tracking information collected is to a specific person. We discuss a case study using vibrations sensors mounted in Virginia Tech's Goodwin Hall in Blacksburg, VA that was recently conducted, demonstrating that similar levels of accuracy in occupant localization can be achieved to current methods, and highlighting the amount of identifying information in the vibration signals dataset. Finally, a method of transforming the vibration data to preserve occupant privacy was proposed and tested on the dataset. The results indicate that our proposed method has successfully resulted in anonymizing the occupant's gender information which was previously identifiable from the vibration data, while minimally impacting the localization accuracy achieved without anonymization.

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Section: B Current Indoor Tracking Methodsmentioning

confidence: 99%

Privacy Concerns Regarding Occupant Tracking in Smart Buildings

Kessler,

Masiane,

Abdelhalim

2020

Preprint

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“…Proposed System 40 Hz Active Bat [8,9] 5 Hz Cricket version 2 [10,11]~1.5 Hz (668 ms) iLoc [36] 1 Hz SNoW Bat [37] 5 Hz…”

Section: Methods Updating Ratementioning

confidence: 99%

“…[10,11] 10-30 Not mentioned Active Bat [8,9] 80 ~140 Dolphin [35] 150 Not mentioned iLoc [36] 20 100 SNoW Bat [37] 15.0 Not mentioned Portable Ultrasonic Indoor [34] 3. [10,11] 10-30 Not mentioned Active Bat [8,9] 80 ~140 Dolphin [35] 150 Not mentioned iLoc [36] 20 100 SNoW Bat [37] 15.0 Not mentioned Portable Ultrasonic Indoor [34] 3. [10,11] 10-30 Not mentioned Active Bat [8,9] 80~140 Dolphin [35] 150 Not mentioned iLoc [36] 20 100 SNoW Bat [37] 15.0 Not mentioned Portable Ultrasonic Indoor [34] 3.4 3.9 Indoor localization application of Frequency Shift Keying and Correlation method [23] Not mentioned 50…”

Section: Least Square Algorithmsmentioning

confidence: 99%

Improvement of Ultrasound-Based Localization System Using Sine Wave Detector and CAN Network

Nguyen

Tran

et al. 2017

JSAN

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This paper presents an improved indoor localization system based on radio frequency (RF) and ultrasonic signals, which we named the SNSH system. This system is composed of a transmitter mounted in a mobile target and a series of receiver nodes that are managed by a coordinator. By measuring the Time Delay of Arrival (TDoA) of RF and ultrasonic signals from the transmitter, the distance from the target to each receiver node is calculated and sent to the coordinator through the CAN network, then all the information is gathered in a PC to estimate the 3D position of the target. A sine wave detector and dynamic threshold filter are applied to provide excellent accuracy in measuring the range from the TDoA results before multilateration algorithms are realized to optimize the accuracy of coordinate determination. Specifically, Linear Least Square (LLS) and Nonlinear Least Square (NLS) techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculation time are carefully considered to ensure optimal system performance and prepare for field application. Experiments show that the sine wave detector algorithm has greatly improved the accuracy of range measurement, with a mean error of 2.2 mm and maximum error of 6.7 mm for distances below 5 m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with the mean error in position remaining under 15 mm. Furthermore, there are 90% confidence error values of 23 mm for LLS and 20 mm for NLS. The update in the overall system has been verified in real system operations, with a maximum rate of 25 ms, which is a better result than many other existing studies.

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