Robust Self-calibration of Constant Offset Time-difference-of-arrival

Batstone, Kenneth; Flood, Gabrielle; Beleyur, Thejasvi; Larsson, Viktor; Goerlitz, Holger R.; Oskarsson, Magnus; Åström, Kalle

doi:10.1109/icassp.2019.8683221

Cited by 8 publications

(10 citation statements)

References 11 publications

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“…Burgess et al [43] proposed solutions for settings where either the sender or the receivers lie in a lower dimensional space. Batstone et al [44] consider the case of constant offset TDOA self-calibration (i.e. transmissions have a known period but unknown offset); this is also a stratified approach which first solves for the unknown time offset [40].…”

Section: A Related Workmentioning

confidence: 99%

Localizing Unsynchronized Sensors with Unknown Sources

Badawy¹,

Larsson²,

Pollefeys³

2021

Preprint

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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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Section: A Related Workmentioning

confidence: 99%

Localizing Unsynchronized Sensors with Unknown Sources

Badawy¹,

Larsson²,

Pollefeys³

2021

Preprint

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“…tacost is an ideal tool to explore the tracking performance of such flexibly placed microphone arrays. Figure 2 shows the microphone array geometry and recording system described in (Batstone et al 2019). In short, the array consisted of 11 microphones, 4 of them on a 120cm tristar, and the remaining 7 microphones attached to the walls of a cave.…”

Section: Localisation Accuracy Of a Multi-microphone Array In The Fieldmentioning

confidence: 99%

tacost: Testing and simulating the performance of acoustic tracking systems

Beleyur

2020

Preprint

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tacost is a Python package to allow the testing of acoustic tracking systems. While many microphone array systems have been characterised analytically and experimentally -these are time-intensive methods. tacost provides a simulation based framework to rapidly assess the tracking behaviour of multiple array geometries, and the dissection of other relevant parameters. This paper explains briefly the design of the package and highlights two example use cases in which the tracking accuracy of different microphone geometries are characterised.

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“…the senders are unsynchronized, or vice versa [5,6]. The constant offset time-difference-of-arrival problem (COTDOA) is similar to the TDOA problem, but the unknown offset is constant [7]. Finally the UTDOA refers to the problem where neither senders nor receivers are synchronized [8].…”

Section: Introductionmentioning

confidence: 99%

“…Considering each combination of calibration type (TOA, TDOA, COTDOA, UTDOA) with each combination of sender/receiver dimensionality within a common framework is a challenge. One strategy to understand and solve the self-calibration problem is to follow a two-tiered stratified approach [6,7]. The first part of this approach is based on solving a relaxed version of the problem where, in the case of TDOA, COTDOA and UTDOA, any offsets are solved for.…”

Section: Introductionmentioning

confidence: 99%

Fast and Robust Stratified Self-Calibration Using Time-Difference-Of-Arrival Measurements

Larsson

Flood

Oskarsson

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper we study the problem of estimating receiver and sender positions using time-difference-of-arrival measurements. For this, we use a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. We present new, efficient solvers for the minimal problems of this low-rank problem. These solvers are used in a hypothesis and test manner to efficiently remove outliers and find an initial estimate which is used for the subsequent step. Once a promising solution is obtained for a sufficiently large subset of the receivers and senders, the solution can be extended to the remaining receivers and senders. These steps are then combined with robust local optimization using the initial inlier set and the initial estimate as a starting point. The proposed system is verified on both real and synthetic data.

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Robust Self-calibration of Constant Offset Time-difference-of-arrival

Cited by 8 publications

References 11 publications

Localizing Unsynchronized Sensors with Unknown Sources

Localizing Unsynchronized Sensors with Unknown Sources

tacost: Testing and simulating the performance of acoustic tracking systems

Fast and Robust Stratified Self-Calibration Using Time-Difference-Of-Arrival Measurements

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