2017
DOI: 10.1121/1.4974289
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Exploiting a geometrically sampled grid in the steered response power algorithm for localization improvement

Abstract: The steered response power phase transform (SRP-PHAT) is a beamformer method very attractive in acoustic localization applications due to its robustness in reverberant environments. This paper presents a spatial grid design procedure, called the geometrically sampled grid (GSG), which aims at computing the spatial grid by taking into account the discrete sampling of time difference of arrival (TDOA) functions and the desired spatial resolution. A SRP-PHAT localization algorithm based on the GSG method is also … Show more

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Cited by 33 publications
(28 citation statements)
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“…The environment is assumed reverberant and affected by stationary noise, which corrupts the acoustic signal emitted by the moving target. The localization of an acoustic source located in r s k at time k is performed through a steered response power algorithm with phase normalization (SRP-PHAT) [28]- [30]. This procedure requires the computation of a generalized cross-correlation (GCC) function between each microphone pair…”
Section: Dynamic Reconfiguration Of the Cluster Of Mobile Agentsmentioning
confidence: 99%
See 1 more Smart Citation
“…The environment is assumed reverberant and affected by stationary noise, which corrupts the acoustic signal emitted by the moving target. The localization of an acoustic source located in r s k at time k is performed through a steered response power algorithm with phase normalization (SRP-PHAT) [28]- [30]. This procedure requires the computation of a generalized cross-correlation (GCC) function between each microphone pair…”
Section: Dynamic Reconfiguration Of the Cluster Of Mobile Agentsmentioning
confidence: 99%
“…The conventional localization algorithm searches a uniformly spaced grid, not taking into account the spatial accuracy characteristics of the sensor network. To compute a sensitivity measure of a given spatial configuration of the sensors we rely on the geometrically sampled grid (GSG) algorithm proposed in [30], which provides a measure of the sensor network localization accuracy in the surrounding region. The GSG array sensitivity is defined as a function δ(r) that provides the number of discrete hyperboloids related to sensor pairs and intersecting in the position r. The sensitivity function provides a measure of the density of the TDOA information over the spatial search grid and, thus, defines a measure of localization accuracy by identifying those areas for which the sensing system is more accurate.…”
Section: Dynamic Reconfiguration Of the Cluster Of Mobile Agentsmentioning
confidence: 99%
“…Several SRP search algorithms have been proposed to reduce computational loads and enable realtime operation. These can be broadly categorized into three main areas: 1) Regional reduction through TDOA-based candidate location mapping (Lathoud and Magimai-Doss 2005;Dmochowski, et al 2008Salvati et al 2017;Oualil, et al 2013), 2) Iterative-based search techniques (Wax and Kailath 1983;Marti et al 2013), and 3) Regional contraction using coarse-to-fine grid searching (Zotkin and Duraiswami 2004;Peterson and Kyriakakis 2005;Nunes et al 2014), volumetric evaluation (Cobos, et al 2011;Lima et al 2015), or stochastic methods Regional contraction methods perform multiple regional searches using successively smaller grid sizes until the desired accuracy is achieved. In general, the method assigns a coarse grid to the spatial region of concern with SRP values being calculated at each point.…”
Section: Srp Search Algorithmsmentioning
confidence: 99%
“…The overall localization performance of search grid-based SL algorithms varies significantly depending on the structure of the search grid. 10 Search grids with a higher resolution provide more accurate localization but require more computational power. For instance, the complexity of the steered response power 3 algorithm is directly proportional to the number of search grid points.…”
Section: Introductionmentioning
confidence: 99%
“…11 Using this concept, Salvati et al introduced the geometrically sampled grid structure under a near-field assumption scenario. 10 Lee et al also introduced the pseudo-uniform grid (PUG) structure, where the grid points are uniformly distributed in the TDoA domain in three-dimensional (3D) space under a far-field assumption scenario. 12 However, the number of grid points generated from the conventional algorithms are defined by the spatial resolution.…”
Section: Introductionmentioning
confidence: 99%