In this paper, we propose a robust and low-complexity acoustic source localization technique based on time differences of arrival (TDOA), which addresses the scenario of distributed sensor networks in 3D environments. Network nodes are assumed to be unsynchronized, i.e., TDOAs between microphones belonging to different nodes are not available. We begin with showing how to select feasible TDOAs for each sensor node, exploiting both geometrical considerations and a characterization of the overall generalized cross correlation (GCC) shape. We then show how to localize sources in the space-range reference frame, where TDOA measurements have a clear geometrical interpretation that can be fruitfully used in the scenario of unsynchronized sensors. In this framework, in fact, the source corresponds to the apex of a hypercone passing through points described by the sole microphone positions and TDOA measurements. The localization problem is therefore approached as a hypercone fitting problem. Finally, in order to improve the robustness of the estimate, we include an outlier detection procedure based on the evaluation of the hypercone fitting residuals. A refinement of source location estimate is then performed ignoring the contributions coming from outlier measurements. A set of simulations shows the performance of individual blocks of the system, with particular focus on the effect of TDOA selection on source localization and refinement steps. Experiments on real data validate the localization algorithm in an everyday scenario, proving that good accuracy can be obtained while saving computational cost in comparison with state-of-the-art techniques.

In this paper, we complete the study of the geometry of the TDOA map that encodes the noiseless model for the localization of a source from the range differences between three receivers in a plane, by computing the Cartesian equation of the bifurcati

In this paper, we propose a novel acoustic source localization method that accommodates the general scenario of multiple independent microphone arrays. The method is based on a 3-D parameter space defined by the 2-D spatial location of a source and the range difference extracted from the time difference of arrival (TDOA). In this space, the set of points that correspond to a given range lie on a circle that expand as the range increases, forming a cone whose apex is the actual location of the source. In this parameter space, the lack of synchronization between arrays results in the fact that clusters of data associated to individual arrays are free to shift along the range axis. The cone constraint, in fact, enables the realignment of such clusters while positioning the cone vertex (source location), thus resulting in a joint data re-synchronization and source localization. We also propose a novel and general analysis methodology for swiftly assessing the localization error as a function of the TDOA uncertainties, which is remarkably accurate for small localization bias. With the aid of this method, simulations and experiments on real data, we show that the cone-fitting process offers excellent localization accuracy in the scenario of multiple unsynchronized arrays, as well as in simpler single-array scenarios, also in comparison with state-of-the-art techniques. We also show that the proposed method offers the desired flexibility for adapting to arbitrary geometries of microphone clusters.