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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Collaborative Localization With Received-Signal Strength in Wireless Sensor Networks
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Collaborative Localization With Received-Signal Strength in Wireless Sensor Networks

机译：无线传感器网络中具有接收信号强度的协同定位

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摘要

In this paper, we present an in-depth study of two collaborative-localization methods, called the multidimensional scaling (MDS) and maximum-likelihood estimator (MLE), for wireless sensor networks. From theoretical analysis, it is shown that MLE is more appropriate than MDS, given the underlying assumption of statistical signal models of the received-signal-strength-based localization problem. We also show that MDS can approximately achieve asymptotic efficiency with appropriate weighting schemes in some scenarios. From extensive simulation results, it is noted that the nonlinear least square algorithms that are commonly used to determine MLE are not as efficient as the iterative MDS algorithms. Thus, we propose a new integrated method MDS-MLE to effectively benefit from the strength of both methods. In the new method, MDS is used as an initialization method for MLE. With the solution of MDS as an initial value, MLE converges much faster and achieves significantly better performance than with random initial values. Superior performance of the new method is clearly demonstrated through simulation results. The effects of the deployment density of sensor nodes and reference nodes (RNs), as well as the deployment structure of RNs, are also studied through various simulations.
机译：在本文中，我们对无线传感器网络的两种协作定位方法（多维缩放（MDS）和最大似然估计器（MLE））进行了深入研究。从理论分析来看，考虑到基于接收信号强度的定位问题的统计信号模型的基本假设，MLE比MDS更合适。我们还表明，在某些情况下，MDS通过适当的加权方案可以大致实现渐近效率。从大量的仿真结果中可以注意到，通常用于确定MLE的非线性最小二乘算法的效率不如迭代MDS算法高。因此，我们提出了一种新的集成方法MDS-MLE以有效地受益于这两种方法的优势。在新方法中，MDS被用作MLE的初始化方法。使用MDS的解决方案作为初始值，与随机初始值相比，MLE收敛更快，并且性能显着提高。仿真结果清楚地表明了该新方法的优越性能。还通过各种模拟研究了传感器节点和参考节点（RN）的部署密度以及RNs的部署结构的影响。

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