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Source localization in large-scale asynchronous sensor networks
Digital Signal Processing ( IF 2.9 ) Pub Date : 2020-11-30 , DOI: 10.1016/j.dsp.2020.102920
Fuhe Ma , Zhang-Meng Liu , Le Yang , Fucheng Guo

The problem of uncooperative source localization and synchronization in asynchronous sensor networks has been considered previously in a centralized manner, where all the raw measurements are delivered to a processing center. However, for large-scale sensor networks the transmission of raw measurements over the networks requires considerable communication overhead, besides being vulnerable to transmission failure and interference, and the computational load at the processing center is rather heavy. A more suitable candidate to this problem is the distributed estimation considered in this paper, where each sensor node communicates with its neighboring nodes only, and the parameters are estimated at each sensor via information cooperation. In order to decouple the unknown source positions and sensor clock offsets, we propose to update them iteratively based on the belief propagation (BP) framework. To reduce the complexity of parameter update at each sensor, the sigma point based estimation method is adopted. Theoretical analyses concerning the computational complexity and communication overhead are carried out. Simulation results demonstrate that the proposed method could estimate the source positions distributively with much lower complexity and communication overhead compared with the centralized methods at the cost of acceptable performance degradation.



中文翻译:

大规模异步传感器网络中的源定位

先前已经以集中的方式考虑了异步传感器网络中不合作的源定位和同步的问题,在该方式中,所有原始测量值都被传送到处理中心。但是,对于大型传感器网络,通过网络传输原始测量值不仅需要传输失败和干扰,而且还需要相当大的通信开销,并且处理中心的计算量相当大。解决此问题的一个更合适的选择是本文中考虑的分布式估计,其中每个传感器节点仅与其相邻节点进行通信,并且通过信息协作在每个传感器处估计参数。为了解耦未知的源位置和传感器时钟偏移,我们建议基于信念传播(BP)框架迭代地更新它们。为了降低每个传感器的参数更新的复杂性,采用了基于sigma点的估计方法。进行了有关计算复杂度和通信开销的理论分析。仿真结果表明,与集中式方法相比,所提出的方法能够以较低的复杂度和通信开销来分布式地估计源位置,但以可接受的性能下降为代价。

更新日期:2020-12-07
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