Self-Localization based on Ambient Signals

Wendeberg, Johannes; Janson, Thomas; Schindelhauer, Christian

doi:10.1016/j.tcs.2011.11.024

Cited by 17 publications

(14 citation statements)

References 14 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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“…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%

“…The latter is a critical factor for TDOA localization. In a 802.11g network we achieve a precision of 0.1 ms by using a method based on message exchange between the peers [22].…”

Section: A Experimental Setupmentioning

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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“…Thrun adapted the original SFM algorithm to the joint localization of microphones and acoustic events with unknown emission times [8], which he calls structure from sound (SFS). This problem has received considerable attention and many methods have been proposed, including both near-field [9][10][11][12] and far-field assumptions [13,14]. The former leads to variants of multidimensional scaling (MDS).…”

Section: Introductionmentioning

confidence: 99%

“…A number of authors generalized Thrun's initial work. A similar setup appears in [13], with the goal of localizing passive nodes in a communication network using ambient radio or sound signals. The proposed ellipsoid time difference of arrival (TDOA) method gives a closed form solution, but it requires a set of measurements with no This work was supported by the Swiss National Science Foundation grant number 20FP-1 151073, "Inverse Problems regularized by Sparsity".…”

Section: Introductionmentioning

confidence: 99%

Structure from Sound with Incomplete Data

Kreković

Baechler

Vetterli

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we consider the problem of jointly localizing a microphone array and identifying the direction of arrival of acoustic events. Under the assumption that the sources are in the far field, this problem can be formulated as a constrained low-rank matrix factorization with an unknown column offset. Our focus is on handling missing entries, particularly when the measurement matrix does not contain a single complete column. This case has not received attention in the literature and is not handled by existing algorithms, however it is prevalent in practice. We propose an iterative algorithm that works with pairwise differences between the measurements eliminating the dependence on the unknown offset. We demonstrate state-ofthe-art performance both in terms of accuracy and versatility.Index Terms-Low-rank matrix factorization, node localization, sensor array self-calibration, far field, missing and uncertain data.

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Self-Localization based on Ambient Signals

Cited by 17 publications

References 14 publications

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

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

Structure from Sound with Incomplete Data

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