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首页> 外文会议>2016 IEEE International Workshop on Acoustic Signal Enhancement >Structured total least squares based internal delay estimation for distributed microphone auto-localization
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Structured total least squares based internal delay estimation for distributed microphone auto-localization

机译：基于结构化总最小二乘法的内部延迟估计，用于分布式麦克风自动定位

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摘要

Auto-localization in wireless acoustic sensor networks (WASNs) can be achieved by time-of-arrival (TOA) measurements between sensors and sources. Most existing approaches are centralized, and they require a fusion center to communicate with other nodes. In practice, WASN topologies are time-varying with nodes joining or leaving the network, which poses scalability issues for such algorithms. In particular, for an increasing number of nodes, the total transmission power required to reach the fusion center increases. Therefore, in order to facilitate scalability, we present a structured total least squares (STLS) based internal delay estimation for distributed microphone localization where the internal delay refers to the time taken for a source signal reaching a sensor to that it is registered as received by the capture device. Each node only needs to communicate with its neighbors instead of with a remote host, and they run an STLS algorithm locally to estimate local internal delays and positions (i.e., its own and those of its neighbors), such that the original centralized computation is divided into many subproblems. Experiments demonstrate that the decentralized internal delay estimation converges to the centralized results with increasing signal-to-noise ratio (SNR). More importantly, less computational complexity and transmission power are required to obtain comparable localization accuracy.
机译：无线声传感器网络（WASN）中的自动定位可以通过传感器与声源之间的到达时间（TOA）测量来实现。大多数现有方法都是集中式的，并且它们需要融合中心才能与其他节点进行通信。实际上，WASN拓扑随节点加入或离开网络而随时间变化，这为此类算法带来了可伸缩性问题。特别地，对于越来越多的节点，到达融合中心所需的总传输功率增加。因此，为了促进可伸缩性，我们针对分布式麦克风定位提出了一种基于结构化最小二乘（STLS）的内部延迟估计，其中内部延迟是指源信号到达传感器并记录为被接收所花费的时间。捕获设备。每个节点仅需要与其邻居而不是与远程主机进行通信，并且它们在本地运行STLS算法以估计本地内部延迟和位置（即，其自身及其邻居的延迟和位置），以便划分原始的集中式计算。变成许多子问题。实验表明，随着信噪比（SNR）的增加，分散的内部延迟估计收敛到集中的结果。更重要的是，需要较少的计算复杂度和传输功率即可获得可比的定位精度。

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