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Distance-based maximum likelihood estimation method for node localization in wireless sensor networks

机译:无线传感器网络中节点定位的基于距离的最大似然估计方法

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Node localization is an important supporting technique in wireless sensor networks (WSNs). The traditional maximum likelihood estimation localization method (MLE) assumes that measurement errors are independent of the distance between anchor node and target node. However, the assumption may contradict with the physical characteristic of some existing measurement techniques, such as the widely-used received signal strength indicator. To address this issue, we propose a distance-based MLE considering the dependence of measurement errors on the distance. The proposed distance-based MLE is formulated as a complicated nonlinear optimization problem. An exact solution method is presented based on first order optimal conditional and to improve the search efficiency, a two-dimensional search method is also given. Simulation experiments are performed to demonstrate the effectiveness of this localization. The simulation results show that the distance-based localization method has better localization accuracy compared with other range-based localization methods.
机译:节点本地化是无线传感器网络(WSN)中的重要支持技术。传统的最大似然估计定位方法(MLE)假设测量误差与锚节点和目标节点之间的距离无关。然而,假设可以与一些现有测量技术的物理特性相矛盾,例如广泛使用的接收信号强度指示器。为了解决这个问题,我们提出了一个基于距离的MLE,考虑到测量误差在距离上的依赖性。所提出的距离的MLE被制定为复杂的非线性优化问题。基于第一订单最佳条件并提高搜索效率,还给出了精确的解决方案方法,还给出了二维搜索方法。进行仿真实验以证明这种本地化的有效性。仿真结果表明,与其他基于范围的定位方法相比,距离的定位方法具有更好的定位精度。

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