DOA-based microphone array postion self-calibration using circular statistics

Jacob, Florian; Schmalenstroeer, Joerg; Haeb‐Umbach, Reinhold

doi:10.1109/icassp.2013.6637620

Cited by 23 publications

(20 citation statements)

References 11 publications

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“…. , rp,J,J+1 ] T , (15) where the first L elements of rp group TDOAs between a fixed PA microphone and varying SA microphones, i.e., j = 1 and l = 1, . .…”

Section: A ML Estimatementioning

confidence: 99%

“…Work on passive calibration includes [8]- [10], [15]- [17]. Chen et al [8] assumed acoustic sources and microphones laying on a 2D plane and used energy measurements to estimate positions of both simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Hon et al [10] assumed acoustic sources at end-fire locations to estimate microphone pairwise distances using TDOA measurements. [15]- [17] assumed sources at far field and individual nodes equipped with a synchronized microphone array capable of reliable DOA estimation. These methods use DOAs observed at individual nodes and TDOAs observed between the microphones of different nodes to estimate the WASN geometry.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Calibration and Synchronization of Two Arrays of Microphones and Loudspeakers Using Particle Swarm Optimization

Kovalyov¹,

Patel²,

Panahi³

2021

IEEE Open J. Signal Process.

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This work presents a methodology for the joint calibration and synchronization of two arrays of microphones and loudspeakers. The problem is modeled as estimation of the rigid motion of one array with respect to the other, as well as estimation of the synchronization mismatch between the two. The proposed method uses dedicated signals emitted by the loudspeakers of the two arrays to compute a set of time of arrival (TOA) estimates. Through a simple transformation, estimated TOAs are converted into a set of linearly independent time difference of arrival (TDOA) measurements, which are modeled by a system of nonlinear equations in the unknown parameters of interest. A maximum likelihood estimate is then given as the solution to a nonlinear weighted least squares (NWLS) problem, which is optimized applying a parallelizable variant of Particle Swarm Optimization (PSO). In this paper, we also derive the Cramér-Rao lower bound (CRLB), and benchmark it against the proposed method in a series of Monte Carlo (MC) simulations. Results show that the proposed method attains high-performance comparable to the CRLB.

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“…. , rp,J,J+1 ] T , (15) where the first L elements of rp group TDOAs between a fixed PA microphone and varying SA microphones, i.e., j = 1 and l = 1, . .…”

Section: A ML Estimatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Calibration and Synchronization of Two Arrays of Microphones and Loudspeakers Using Particle Swarm Optimization

Kovalyov¹,

Patel²,

Panahi³

2021

IEEE Open J. Signal Process.

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“…However, TDOA based approach requires three beacon positions to localize the sensor node and cannot be used alone to find the orientation of the sensor node [2], [3]. Node location and orientation can also be calibrated using DOA based algorithms using triangulation requiring three known beacon positions [4], [5], [10]. The DOA based approach also does not require clock synchronization among the beacon and sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Acoustic sensor network relative self-calibration using joint TDOA and DOA with unknown beacon positions

Anwar

Maqbool

Rehman

et al. 2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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In this paper, we propose an efficient self-calibration mechanism, achieving localization as well as orientation, for wireless acoustic sensor network. Time difference of arrival (TDOA) based on wireless and acoustic signals, while direction of arrival (DOA) based on acoustic only, are used jointly to achieve acoustic sensor node calibration. Each sensor node is equipped with three microphones and a wireless transceiver. Sensor nodes are calibrated by using two beacon positions with respect to a reference node whose position is assumed to be known. Our approach requires a single moving beacon (MB) equipped with RF and acoustic signal sources. Once a node has been calibrated (in terms of location and orientation) it can be considered as a valid reference for the remaining uncalibrated nodes. We have developed hardware platform to validate the proposed calibration mechanism. Performance results show the effectiveness and usefulness of the proposed mechanism.

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“…However, a clock synchronisation of the A/D converters of the distributed microphones is still required. Even this requirement becomes obsolete if direction of arrival (DoA) based techniques are employed [7,8]. TDoA and DoA based calibration if carried out with artificial calibration signals with appropriate correlation properties, will typically achieve higher accuracy compared to speech signal based approaches [9].…”

Section: Introductionmentioning

confidence: 99%

Absolute Geometry Calibration of Distributed Microphone Arrays in an Audio-Visual Sensor Network

Jacob¹,

Haeb‐Umbach²

2015

Preprint

Self Cite

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Joint audio-visual speaker tracking requires that the locations of microphones and cameras are known and that they are given in a common coordinate system. Sensor selflocalization algorithms, however, are usually separately developed for either the acoustic or the visual modality and return their positions in a modality specific coordinate system, often with an unknown rotation, scaling and translation between the two. In this paper we propose two techniques to determine the positions of acoustic sensors in a common coordinate system, based on audio-visual correlates, i.e., events that are localized by both, microphones and cameras separately. The first approach maps the output of an acoustic self-calibration algorithm by estimating rotation, scale and translation to the visual coordinate system, while the second solves a joint system of equations with acoustic and visual directions of arrival as input. The evaluation of the two strategies reveals that joint calibration outperforms the mapping approach and achieves an overall calibration error of 0.20 m even in reverberant environments.

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DOA-based microphone array postion self-calibration using circular statistics

Cited by 23 publications

References 11 publications

Joint Calibration and Synchronization of Two Arrays of Microphones and Loudspeakers Using Particle Swarm Optimization

Joint Calibration and Synchronization of Two Arrays of Microphones and Loudspeakers Using Particle Swarm Optimization

Acoustic sensor network relative self-calibration using joint TDOA and DOA with unknown beacon positions

Absolute Geometry Calibration of Distributed Microphone Arrays in an Audio-Visual Sensor Network

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