Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

Kraljević, Luka; Russo, Mladen; Stella, Maja; Sikora, Marjan

doi:10.1109/access.2020.2993076

Cited by 28 publications

(13 citation statements)

References 53 publications

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“…The work by Oguz-Ekim et al [3] considers outlier measurements, by assuming measurement errors with Laplace distribution. Other relevant literature on range-based target localization is [4]- [10].…”

Section: A Related Workmentioning

confidence: 99%

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Robust Localization with Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations

Domingos,

Soares,

Xavier

2021

Preprint

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Locating an object is key in many applications, namely in high-stakes real-world scenarios, like detecting humans or obstacles in vehicular networks. In such applications, pointwise estimates are not enough, and the full area of locations compatible with acquired measurements should be available, for robust and safe navigation. This paper presents a scalable algorithm for creating a superset of all possible target locations, given range measurements with bounded error. The assumption of bounded error is mild, since both hardware characteristics and application scenario impose upper bounds on measurement errors, and the bounded set can be taken from confidence regions of the error distributions. We construct the superset through convex relaxations that use Linear Fractional Representations (LFRs), a well-known technique in robust control. Additionally, we also provide a statistical interpretation for the set of possible target positions, considering the framework of robust estimation. Finally, we provide empirical validation by comparing our LFR method with a standard semidefinite relaxation. Our approach has shown to pay off for small to moderate noise levels: the supersets created by our method are tighter than the benchmark ones being about 20% smaller in size. Furthermore, our method tends to be tight because the size of the supersets is, on median terms, within a 3% margin of a lower bound computed via grid search.

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Section: A Related Workmentioning

confidence: 99%

“…In (10), q is the number of rows of C. The LFR is determined by the positive integer p and by the matrices B and C, and the vectors a and d, and is assumed to be well-posed on its domain U (meaning that matrix I − C(I p ⊗ U ) is assumed to be invertible for each matrix U in the set U).…”

Section: Background On Linear-fractional Representationsmentioning

confidence: 99%

Robust Localization with Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations

Domingos,

Soares,

Xavier

2021

Preprint

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“…Based on different arrays of acoustic sensors, passive acoustic source localization techniques using the time-difference method [1] have been widely used in the fields of speech enhancement, artillery localization, hydroacoustic detection, etc [2,3,4]. In recent years, the research on target localization using acoustic waves has been carried out one after another, and more complete solutions to this problem have been proposed successively.…”

Section: Introductionmentioning

confidence: 99%

Research on fast acoustic location algorithm and error analysis based on five-element four-sided cone array

Wang,

Han,

Sun

et al. 2024

Third International Conference on Computer Technology, Information Engineering, and Electron Materials (CTIEEM 2023)

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Passive source localization based on time difference of arrival (TDOA) is an important method for high-precision and fast localization of point sources in areas such as gunshot localization and remote missile landing point localization. Traditional multi-element array passive sound localization algorithms have nonlinear equation, and their analytical solutions are obtained through complex transformations, which may result in non-unique solutions and low localization accuracy, making them unsuitable for practical applications. In this paper, a five-element tetrahedral array localization model is proposed, which utilizes the redundant degrees of freedom contained in the five-element array and introduces an intermediate variable (redundant variable) to linearize the resulting equation system. The accuracy of the analytical solution obtained by this optimization algorithm and the directional and distance errors under this array model are analyzed.

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“…Researchers developed a hybrid TDOA/AOA positioning algorithm as a solution to the aforementioned issues with AOA or TDOA [ 16 ]. The TOA-AOA positioning method combines the TOA and AOA positioning techniques.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

Cao

Chen

et al. 2023

Biomimetics

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A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization mechanism on the basis of enhancing the performance of the original algorithm. To obtain a better fitness value throughout the optimization process and increase the algorithm’s optimization accuracy, the fitness function based on maximum likelihood estimation is modified. In order to speed up algorithm convergence and decrease needless global search without compromising population diversity, an initial solution is simultaneously added to the starting population location. Simulation findings demonstrate that the suggested method outperforms the TDOA/AOA algorithm and other comparable algorithms, including Taylor, Chan, PSO, CPSO, and basic CSA algorithms. The approach performs well in terms of robustness, convergence speed, and node positioning accuracy.

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Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

Cited by 28 publications

References 53 publications

Robust Localization with Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations

Robust Localization with Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations

Research on fast acoustic location algorithm and error analysis based on five-element four-sided cone array

Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

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