In this work, we propose a novel approach for distributed 3D sound source localization and tracking based on networks of planar microphone arrays, each of which estimates a 2D Direction Of Arrival (DOA). The proposed method is computationally distributed and eliminates the need for a specialized node to collect and process all information. Sound source localization is achieved by considering the task as a distributed optimization problem approached using the Adapt Then Combine (ATC) diffusion technique. This approach also allows the development of cooperation strategies between sensor nodes (i.e., microphone arrays). We propose the use of a cooperation strategy that improves the localization accuracy by exploiting the estimated error statistics of each sensor node and penalizing the noisy arrays. We then evaluate the proposed approach in terms of localization accuracy and robustness to noisy sensor measurements.

Differential Microphone Arrays (DMAs) are of great interest in the literature on small-sized microphone arrays, due to their good directivity properties and nearly frequency-invariant spatial responses. Recently developed beamforming techniques combine multiple DMA units to form flexible two-stage spatial filtering systems, where the output of each DMA is fed into a higher-level filter, called virtual filter, for further processing. In this manuscript, we analyze and discuss some properties of a broad class of two-stage beamformers with arbitrary planar geometry. In this context, the DMA units are all assumed to have the same directivity pattern of arbitrary order and can be characterized by a variable number of omnidirectional sensors organized in an arbitrary geometry. For any given choice of the virtual array filter, we introduce a closed-form optimization procedure to design DMA filters that maximize the White Noise Gain (WNG) or the Directivity Factor (DF) of the resulting two-stage beamformer at any frequency. Based on this frequency-dependent design, we propose a frequency-invariant design of the two-stage beamformer and we compare the performance of the two approaches. Finally, we propose two possible computational schemes for the proposed generic two-stage spatial filtering system and discuss their efficiency in performing filtering, steering, and changing beampattern.