Structured total least squares based internal delay estimation for distributed microphone auto-localization

Zhang, Jie; Hendriks, Richard C.; Heusdens, Richard

doi:10.1109/iwaenc.2016.7602958

“…Multidimensional unfolding [21], [22], ML optimization [14] Distances between nodes/events -Requires full synchronization, Bad local minima SDP relaxations [23], [24], [25], [26], [27], [28], [29] Distances/TDOA/FDOA -Requires anchor nodes or positions of the sensor nodes Majorization [30], Two-stage [31], [32], [33], [34] TDOA Source or receiver offsets Bad local minima, cannot handle near-minimal configurations Two-stage [16] TDOA Source & receiver offsets Slow, cannot handle near-minimal configurations Proposed TOA/TDOA Source & receiver offsets -A more common situation in audio applications is that the nodes can only receive or only send. The "sending" nodes need not be real devices; they can be any acoustic events or signals of opportunity.…”

Section: Approachmentioning

confidence: 99%

“…An alternative approach is to estimate the unknowns sequentially, first recovering the unknown times, and then using them to recover the geometry [31], [32]. Early work of Pollefeys and Nister [38] exploits the low rank of a certain matrix of squared TOA differences.…”

Section: Approachmentioning

confidence: 99%

Localizing Unsynchronized Sensors With Unknown Sources

Badawy

¹

,

Larsson

²

,

Pollefeys

³

2023

IEEE Trans. Signal Process.

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We propose a method for sensor array selflocalization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known. Unlike previous work, our method directly recovers the array geometry, instead of first estimating the timing information. The key component is a new loss function which is insensitive to the unknown timings. We cast the problem as a minimization of a non-convex functional of the Euclidean distance matrix of microphones and sources subject to certain non-convex constraints. After convexification, we obtain a semidefinite relaxation which gives an approximate solution; subsequent refinement on the proposed loss via the Levenberg-Marquardt scheme gives the final locations. Our method achieves state-of-the-art performance in terms of reconstruction accuracy, speed, and the ability to work with a small number of sources and receivers. It can also handle missing measurements and exploit prior geometric and temporal knowledge, for example if either the receiver offsets or the emission times are known, or if the array contains compact subarrays with known geometry.

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“…An alternative approach is to estimate the unknowns sequentially, first recovering the unknown times, and then using them to recover the geometry [26], [27]. Early work of Pollefeys and Nister [28] exploits the low rank of a certain matrix of squared TOA differences.…”

Section: A Related Workmentioning

confidence: 99%

Localizing Unsynchronized Sensors with Unknown Sources

Badawy¹,

Larsson²,

Pollefeys³

2021

Preprint

0

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We propose a method for sensor array selflocalization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known. Unlike previous work, our method directly recovers the array geometry, instead of first estimating the timing information. The key component is a new loss function which is insensitive to the unknown timings. We cast the problem as a minimization of a non-convex functional of the Euclidean distance matrix of microphones and sources subject to certain non-convex constraints. After convexification, we obtain a semidefinite relaxation which gives an approximate solution; subsequent refinement on the proposed loss via the Levenberg-Marquardt scheme gives the final locations. Our method achieves state-of-the-art performance in terms of reconstruction accuracy, speed, and the ability to work with a small number of sources and receivers. It can also handle missing measurements and exploit prior geometric and temporal knowledge, for example if either the receiver offsets or the emission times are known, or if the array contains compact subarrays with known geometry.

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“…Notice that this solver depends on knowledge on a. To estimate (the direct path of) a one can use a source localization algorithm, e.g., [35]- [37], in combination with the sensor locations, or use the generalized eigenvalue decomposition of the matrices R nn and R yy [38] [39].…”

Section: B Solver Based On the Steering Vector Amentioning

confidence: 99%

Microphone Subset Selection for MVDR Beamformer Based Noise Reduction

Zhang

¹

,

Chepuri

²

,

Hendriks

³

et al. 2018

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

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In large-scale wireless acoustic sensor networks (WASNs), many of the sensors will only have a marginal contribution to a certain estimation task. Involving all sensors increases the energy budget unnecessarily and decreases the lifetime of the WASN. Using microphone subset selection, also termed as sensor selection, the most informative sensors can be chosen from a set of candidate sensors to achieve a prescribed inference performance. In this paper, we consider microphone subset selection for minimum variance distortionless response (MVDR) beamformer based noise reduction. The best subset of sensors is determined by minimizing the transmission cost while constraining the output noise power (or signal-to-noise ratio). Assuming the statistical information on correlation matrices of the sensor measurements is available, the sensor selection problem for this model-driven scheme is first solved by utilizing convex optimization techniques. In addition, to avoid estimating the statistics related to all the candidate sensors beforehand, we also propose a data-driven approach to select the best subset using a greedy strategy. The performance of the greedy algorithm converges to that of the model-driven method, while it displays advantages in dynamic scenarios as well as on computational complexity. Compared to a sparse MVDR or radius-based beamformer, experiments show that the proposed methods can guarantee the desired performance with significantly less transmission costs.

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Structured total least squares based internal delay estimation for distributed microphone auto-localization

Cited by 8 publications

References 21 publications

Localizing Unsynchronized Sensors With Unknown Sources

Localizing Unsynchronized Sensors With Unknown Sources

Localizing Unsynchronized Sensors with Unknown Sources

Microphone Subset Selection for MVDR Beamformer Based Noise Reduction

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