Distributed-Robust MVDR Beamforming With Energy-Efficient Topology Control in Wireless Acoustic Sensor Networks

We are often surrounded by intelligent devices with one or more acoustic sensors, which constitute a wireless acoustic sensor network (WASN) and can be exploited for various audio/speech processing tasks. As the captured audio signals are inevitably corrupted by ambient noises, signal enhancement is vital in WASNs. To this end, this paper proposes a distributed-robust and energy-efficient MVDR beamformer (BF) for WASNs. Specifically, a distributed MVDR BF is first derived by recursively updating the inverse of the noise correlation matrix, which requires fewer data transmission without sacrificing performance. Then, its robust version is further designed to alleviate the adverse effects of parameter mismatches. Finally, an energy-efficient network topology control (EENTC) is carried out to reduce the energy consumption, by optimizing the weight matrix of the distributed averaging process. Since the proposed EENTC strategy involves non-convex programming, we transform it into a convex one and solve it via the Dinkelbach algorithm. Unlike the centralized BFs, the proposed method works without an additional central processor. Moreover, it is robust against parameter mismatch during beamforming and can reduce a large amount of data transmission. Simulation and real-world experimental results confirm the validity of the proposed method.