基于优化最大似然的无线传感器网络源定位

Mohammed Rahman, Edith Cowan, D. Habibi, I. Ahmad, M. Z. Rahman
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引用次数: 5

摘要

最大似然(ML)是一种广泛应用于各种应用的流行且有效的估计器,目前为无线传感器网络(WSN)中的源定位提供了最准确的估计。然而,ML有两个主要缺点,即它是一个有偏估计器,对参数扰动也高度敏感。介绍了一种ML优化(OML)算法,该算法可以最小化平方和偏差,并在统计估计中表现出优于ML的性能,特别是在有限数据集上。本文提出了一种基于OML估计过程的WSN声源定位新模型。除了使用真实世界现场实验数据进行跟踪移动军用车辆的性能分析外,还对更复杂的源定位和跟踪问题进行了仿真,以验证新的基于oml的模型的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Source Localisation in Wireless Sensor Networks Based on Optimised Maximum Likelihood
Maximum likelihood (ML) is a popular and effective estimator for a wide range of diverse applications and currently affords the most accurate estimation for source localisation in wireless sensor networks (WSN). ML however has two major shortcomings namely, that it is a biased estimator and is also highly sensitive to parameter perturbations. An Optimisation to ML (OML) algorithm was introduced that minimises the sum-of-squares bias and exhibits superior performance to ML in statistical estimation, particularly with finite datasets. This paper proposes a new model for acoustic source localisation in WSN, based upon the OML estimation process. In addition to the performance analysis using real world field experimental data for the tracking of moving military vehicles, simulations have been performed upon the more complex source localisation and tracking problem, to verify the potential of the new OML-based model.
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