提升无线麦克风网络信号估计速度与效率
Improved Topology-Independent Distributed Adaptive Node-Specific Signal Estimation for Wireless Acoustic Sensor Networks
- 允许节点利用邻居部分聚合数据，增加自由度加速收敛
- 新算法收敛速度接近全连接网络的最优解
- 适合低功耗、拓扑不规则的无线麦克风阵列应用
本文针对无线声学传感器网络（WASNs）中拓扑无关的分布式自适应节点特定信号估计（TI-DANSE）问题，提出改进算法TI-DANSE+。传统TI-DANSE在非全连通网络中存在迭代收敛缓慢的问题。TI-DANSE+允许每个节点在更新本地参数时，利用邻居传输的各自主部分聚合信号，从而提升可用自由度，加快收敛速度。同时引入树剪枝策略进一步优化收敛性能。仿真结果表明，该算法收敛速度接近全连通场景下的经典DANSE，同时降低通信功耗，适用于资源受限的无线声学传感网络。
原文摘要 · Abstract (English)
This paper addresses the challenge of topology-independent (TI) distributed adaptive node-specific signal estimation (DANSE) in wireless acoustic sensor networks (WASNs) where sensor nodes exchange only fused versions of their local signals. An algorithm named TI-DANSE has previously been presented to handle non-fully connected WASNs. However, its slow iterative convergence towards the optimal solution limits its applicability. To address this, we propose in this paper the TI-DANSE+ algorithm. At each iteration in TI-DANSE+, the node set to update its local parameters is allowed to exploit each individual partial in-network sums transmitted by its neighbors in its local estimation problem, increasing the available degrees of freedom and accelerating convergence with respect to TI-DANSE. Additionally, a tree-pruning strategy is proposed to further increase convergence speed. TI-DANSE+ converges as fast as the DANSE algorithm in fully connected WASNs while reducing transmit power usage. The convergence properties of TI-DANSE+ are demonstrated in numerical simulations.
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