TDOA-Based Multiple Acoustic Source Localization Without Association Ambiguity

Sundar, Harshavardhan; Sreenivas, T.V.; Seelamantula, Chandra Sekhar

doi:10.1109/taslp.2018.2851147

Cited by 42 publications

(12 citation statements)

References 36 publications

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“…T and the inaccurate coordinates of sensors, according to (9), the positions of the multiple sources can be estimated through minimizing the following log-likelihood function…”

Section: Proposed Methods a Model Constructionmentioning

confidence: 99%

“…When dealing with the task of multi-source localization, one of the challenging problems is the data matching between the measurements received by a set of sensors and the corresponding radiation sources. Hernandez [8], Sundar et al [9], Venkateswaran and Madhow [10], and Shen et al [11] develop several algorithms to tackle the data matching problem when the association information is unknown. Meanwhile, in other cases, the matching of the radiation source with its measured data can be achieved by using the disjointness of the multiple sources [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Multiple Sources Localization Using TOA Measurements Based on Lagrange Programming Neural Network

Jia¹,

Wang²,

Yin³

et al. 2019

IEEE Access

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Multiple sources' localization using the time-of-arrival (TOA) measurements in the presence of sensor position uncertainty is studied in this paper. The non-cooperative scenario where the clock between the source and sensors is not synchronized is also considered. First, we theoretically prove that the Cramér-Rao lower bound of multi-source joint localization is lower than that of the single source case when the TOA measurements from different sources possess the same sensor position errors. Moreover, different from the conventional numerical algorithms, we propose to employ a neural circuit named Lagrange programming neural network (LPNN) to fulfill this non-trivial task of jointly locating multiple sources. The maximum likelihood problem of multi-source localization is reformulated by utilizing the LPNN framework, and then we build up a neural model, which is proved to be asymptotically stable through both mathematical analysis and numerical experiments. The simulation results show that the proposed method is superior to other positioning algorithms, and it has excellent localization performance and robustness even in the case of large measurement noise, synchronization error, and sensor position displacements. INDEX TERMS Multi-source localization, time-of-arrival (TOA), clock asynchronization, sensor position uncertainties, Lagrange programming neural network (LPNN).

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“…T and the inaccurate coordinates of sensors, according to (9), the positions of the multiple sources can be estimated through minimizing the following log-likelihood function…”

Section: Proposed Methods a Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Multiple Sources Localization Using TOA Measurements Based on Lagrange Programming Neural Network

Jia¹,

Wang²,

Yin³

et al. 2019

IEEE Access

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show abstract

“…For localization, it is often desirable to estimate the source directions from TDoA estimates, e.g., using multi-dimensional lookup tables [63], by triangulation using Least Squares (LS) optimization if the array geometry is known a priori [64], [65], or by triangulation based on the intersection of interhyperboloidal spatial regions formed by the TDoA estimates, e.g., [66], [67].…”

Section: A Single-source Localizationmentioning

confidence: 99%

The LOCATA Challenge: Acoustic Source Localization and Tracking

Evers

Löllmann

Mellmann

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

128

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The ability to localize and track acoustic events is a fundamental prerequisite for equipping machines with the ability to be aware of and engage with humans in their surrounding environment. However, in realistic scenarios, audio signals are adversely affected by reverberation, noise, interference, and periods of speech inactivity. In dynamic scenarios, where the sources and microphone platforms may be moving, the signals are additionally affected by variations in the source-sensor geometries. In practice, approaches to sound source localization and tracking are often impeded by missing estimates of active sources, estimation errors, as well as false estimates. The aim of the LOCAlization and TrAcking (LOCATA) Challenge is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking. This article provides a review of relevant localization and tracking algorithms and, within the context of the existing literature, a detailed evaluation and dissemination of the LOCATA submissions. The evaluation highlights achievements in the field, open challenges, and identifies potential future directions.

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“…A well-known problem of these methods is the so-called association ambiguity [5], [6], whereby these methods produce more candidates than there are actual sources. Although some solutions have been proposed [7], [8], this is still an active research area. Differently from DOA combination methods, this paper is concerned with SSL that directly produces a suitable 2D position representation as an output.…”

Section: Introductionmentioning

confidence: 99%

Learning Multiple Sound Source 2D Localization

Moing

Vinayavekhin

Inoue

et al. 2019

2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)

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In this paper, we propose novel deep learning based algorithms for multiple sound source localization. Specifically, we aim to find the 2D Cartesian coordinates of multiple sound sources in an enclosed environment by using multiple microphone arrays. To this end, we use an encoding-decoding architecture and propose two improvements on it to accomplish the task. In addition, we also propose two novel localization representations which increase the accuracy. Lastly, new metrics are developed relying on resolution-based multiple source association which enables us to evaluate and compare different localization approaches. We tested our method on both synthetic and real world data. The results show that our method improves upon the previous baseline approach for this problem.

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TDOA-Based Multiple Acoustic Source Localization Without Association Ambiguity

Cited by 42 publications

References 36 publications

Joint Multiple Sources Localization Using TOA Measurements Based on Lagrange Programming Neural Network

Joint Multiple Sources Localization Using TOA Measurements Based on Lagrange Programming Neural Network

The LOCATA Challenge: Acoustic Source Localization and Tracking

Learning Multiple Sound Source 2D Localization

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