Self-localization application for iPhone using only ambient sound signals

Janson, Thomas; Schindelhauer, Christian; Wendeberg, Johannes

doi:10.1109/ipin.2010.5648094

Cited by 22 publications

(14 citation statements)

References 21 publications

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“…In the far field case, the signals originate from the distance, such that the propagation front of the signals approximates a line, sweeping over the receivers. Then, the positions of receivers and subsequently of the signal directions can be calculated directly [34][35][36].…”

Section: Calibration Phasementioning

confidence: 99%

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

Ens

Höflinger

Wendeberg

et al. 2015

International Journal of Navigation and Observation

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This paper presents an acoustic indoor localization system for commercial smart phones that emit high pitched acoustic signals beyond the audible range. The acoustic signals with an identifier code modulated on the signal are detected by self-built receivers which are placed at the ceiling or on walls in a room. The receivers are connected in a Wi-Fi network, such that they synchronize their clocks and exchange the time differences of arrival (TDoA) of the received chirps. The location of the smart phone is calculated by TDoA multilateration. The precise time measuring of sound enables high precision localization in indoor areas. Our approach enables applications that require high accuracy, such as finding products in a supermarket or guiding blind people through complicated buildings. We have evaluated our system in real-world experiments using different algorithms for calibrationfree localization and different types of sound signals. The adaptive GOGO-CFAR threshold enables a detection of 48% of the chirp pulses even at a distance of 30 m. In addition, we have compared the trajectory of a pedestrian carrying a smart phone to reference positions of an optic system. Consequently, the localization error is observed to be less than 30 cm.

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Section: Calibration Phasementioning

confidence: 99%

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

Ens

Höflinger

Wendeberg

et al. 2015

International Journal of Navigation and Observation

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“…Systems using uncontrolled signals either employ multiple known receivers [13] as part of the system (potentially violating privacy) or use collaborative sensing. Collaborative sensing [14] requires multiple unknown receivers that try to simultaneously localize themselves. Some systems like [15] take a hybrid approach by using both a source and receiver in the client device.…”

Section: Related Workmentioning

confidence: 99%

Indoor localization using controlled ambient sounds

Rishabh

Kimber

Adcock

2012

2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Abstract-Audio-based receiver localization in indoor environments has multiple applications including indoor navigation, location tagging, and tracking. Public places like shopping malls and consumer stores often have loudspeakers installed to play music for public entertainment. Similarly, office spaces may have sound conditioning speakers installed to soften other environmental noises. We discuss an approach to leverage this infrastructure to perform audio-based localization of devices requesting localization in such environments, by playing barely audible controlled sounds from multiple speakers at known positions. Our approach can be used to localize devices such as smart-phones, tablets and laptops to sub-meter accuracy. The user does not need to carry any specialized hardware. Unlike acoustic approaches which use high-energy ultrasound waves, the use of barely audible (low energy) signals in our approach poses very different challenges. We discuss these challenges, how we addressed those, and experimental results on two prototypical implementations: a request-play-record localizer, and a continuous tracker. We evaluated our approach in a real world meeting room and report promising initial results with localization accuracy within half a meter 94% of the time. The system has been deployed in multiple zones of our office building and is now part of a location service in constant operation in our lab.

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“…Some rely on an approximation of the signal propagation, e.g., that the path of the signal is a straight line [18], or that signals are emitted far away. In this far field case the distance vectors between receivers can be approximated [19], [20], or even calculated in closed form [21], [22]. Once the receivers are known, the calculation of the signal direction is straight forward.…”

Section: Related Workmentioning

confidence: 99%

Robust tracking of a mobile beacon using time differences of arrival with simultaneous calibration of receiver positions

Wendeberg

Müller

Schindelhauer

et al. 2012

2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Abstract-Localization based on time differences of arrival (TDOA) has turned out to be a promising approach when neither receiver positions nor the positions of signal origins are known a priori. In this paper, we consider calibration-free tracking of a mobile beacon using TDOA, i.e., the positions of the receivers are not given. We propose a probabilistic formulation using a particle filter to simultaneously localize the signal beacon and the receivers. Our method is robust against measurement outliers and incorrect initialization. This is achieved through a probabilistic sensor model for TDOA data which explicitly considers the measurement uncertainty and takes into account disproportional errors caused by measurement outliers. For the reliable initialization of the particle filter, we apply an iterative optimization approach to multiple subsets of TDOA data, where the best solution is implicitly selected by appropriate weighing of the sensor model. We verify the robustness of our approach in extensive experiments in a spacious indoor environment by an ultrasound beacon moving on various trajectories. We demonstrate that our approach ensures a proper initialization of the particle filter and provides accurate position estimates for the signal beacon and the receivers even in case of measurement outliers. Compared to position references of an optical motion capture system we achieve mean position errors below 5 centimeters.

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Self-localization application for iPhone using only ambient sound signals

Cited by 22 publications

References 21 publications

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

Indoor localization using controlled ambient sounds

Robust tracking of a mobile beacon using time differences of arrival with simultaneous calibration of receiver positions

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