论文标题
无线声传感器网络中的分布式节点特异性块-Diagonal LCMV波束形成
Distributed Node-Specific Block-Diagonal LCMV Beamforming in Wireless Acoustic Sensor Networks
论文作者
论文摘要
本文从集中式线性约束最小方差（LCMV）梁形方面的新型分布淋巴特异性块 - 基因构成线性约束的最小方差线形成的分析解时，考虑到噪声协方差矩阵是块 - 构成块。为了进一步降低所提出的波束形式的计算复杂性，引入了Shermanmorrison-Woodbury公式以计算噪声样品协方差矩阵的反转。通过这样做，可以在节点之间使用较低的尺寸来计算交换的信号，在每个节点上仍然可以使用最佳的LCMV光束器，就好像每个节点都在传输其所有原始传感器信号观测值一样。所提出的波束形式是完全分布的，而不会对基础网络拓扑施加限制或扩展计算复杂性，即，当将新节点添加到网络中时，每种节点复杂性不会增加。与通常具有时间恢复的最新分布式节点特异性算法相比，提出的波束形式正好逐帧最佳地求解LCMV光束器，该算法的计算复杂性较低，并且对声学传递函数估计误差和语音活动探测器误差的计算复杂性更为强大。提出了许多实验结果，以验证所提出的波束形式的有效性。
This paper derives the analytical solution of a novel distributed node-specific block-diagonal linearly constrained minimum variance beamformer from the centralized linearly constrained minimum variance (LCMV) beamformer when considering that the noise covariance matrix is block-diagonal. To further reduce the computational complexity of the proposed beamformer, the ShermanMorrison-Woodbury formula is introduced to compute the inversion of noise sample covariance matrix. By doing so, the exchanged signals can be computed with lower dimensions between nodes, where the optimal LCMV beamformer is still available at each node as if each node is to transmit its all raw sensor signal observations. The proposed beamformer is fully distributable without imposing restrictions on the underlying network topology or scaling computational complexity, i.e., there is no increase in the per-node complexity when new nodes are added to the networks. Compared with state-of-the-art distributed node-specific algorithms that are often time-recursive, the proposed beamformer exactly solves the LCMV beamformer optimally frame by frame, which has much lower computational complexity and is more robust to acoustic transfer function estimation error and voice activity detector error. Numerous experimental results are presented to validate the effectiveness of the proposed beamformer.
