Anchor-free TDOA self-localization

Wendeberg, Johannes; Höflinger, Fabian; Schindelhauer, Christian; Reindl, L.

doi:10.1109/ipin.2011.6071909

Cited by 20 publications

(26 citation statements)

References 27 publications

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“…The "Iterative Cone Alignment" algorithm [39,40] solves iteratively a nonlinear optimization problem of TDoA by a physical spring-mass simulation. The success rate of solving the calculation of the receiver positions was increased to 99.4% (with only six received signals and four receivers).…”

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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“…An elegant approach to general TDOA was presented by Pollefeys and Nister [24] using matrix factorization of the equation system. However, this approach can be only applied when at least eight (respectively ten in the three dimensional case) receivers are available which is twice the minimum number [25]. Furthermore, this technique appears to be prone to TDOA measurement errors.…”

Section: Related Workmentioning

confidence: 99%

“…These approaches require proper a initialization and tend to get stuck in local minima of the error function. Multiple repeated attempts with random initialization were proven to solve almost all simulated random scenarios [25], but the efficient initialization of a scenario remains an open problem. Besides, the iterative approaches, while robust to Gaussian errors, are still prone to disproportional errors, caused by echoes from walls, and by signal detection errors in the receivers.…”

Section: Related Workmentioning

confidence: 99%

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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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“…Initialization of TDOA networks is studied in (Pollefeys and Nister, 2008), where solutions were give to two non-minimal cases of ten transmitters and five receivers, whereas the minimal solution for far field approximation in this paper are six transmitters and four receivers. In (Wendeberg et al, 2011) a TDOA setup is used for indoor navigation based on non-linear optimization, but the method can get stuck in local minima and is dependent on initialization.…”

Section: Introductionmentioning

confidence: 99%

Minimal Structure and Motion Problems for TOA and TDOA Measurements with Collinearity Constraints

Ask

Burgess

Åström

2013

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

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Abstract:Structure from sound can be phrased as the problem of determining the position of a number of microphones and a number of sound sources given only the recorded sounds. In this paper we study minimal structure from sound problems in both TOA (time of arrival) and TDOA (time difference of arrival) settings with collinear constraints on e.g. the microphone positions. Three such minimal cases are analyzed and solved with efficient and numerically stable techniques. An experimental validation of the solvers are performed on both simulated and real data. In the paper we also show how such solvers can be utilized in a RANSAC framework to perform robust matching of sound features and then used as initial estimates in a robust non-linear leastsquares optimization.

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Anchor-free TDOA self-localization

Cited by 20 publications

References 27 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

Minimal Structure and Motion Problems for TOA and TDOA Measurements with Collinearity Constraints

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