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RSS Localization Under Gaussian Distributed Path Loss Exponent Model
IEEE Wireless Communications Letters ( IF 6.3 ) Pub Date : 2021-01-01 , DOI: 10.1109/lwc.2020.3021991
K. N. R. Surya Vara Prasad , Vijay K. Bhargava

We consider localization from the received signal strength (RSS) when the transmit power and the log-distance pathloss exponent (PLE) are unknown. The unknown transmit power problem is handled by working with the difference of RSS (DRSS) from a reference node. The unknown PLE is statistically modelled as a Gaussian distributed random variable. A maximum-likelihood estimation procedure is firstly proposed to obtain the ratio-of-distances in closed-form. Next, in order to obtain the source location from the ratio-of-distance estimates, we propose a two-step linear least squares (TLLS) estimator which exploits the known relation between the source coordinates and the range variable. Finally, we propose a maximum-a-posteriori (MAP) estimator which jointly estimates the source location and the PLE by maximizing the posterior likelihood of the DRSS values, given the distribution of the PLE. Numerical studies validate the improved localization accuracy of the proposed estimators over the state-of-the-art.

中文翻译:

高斯分布路径损耗指数模型下的RSS定位

当发射功率和对数距离路径损耗指数 (PLE) 未知时,我们从接收信号强度 (RSS) 中考虑定位。未知发射功率问题是通过处理来自参考节点的 RSS (DRSS) 差异来处理的。未知 PLE 被统计建模为高斯分布随机变量。首先提出了一种最大似然估计程序来获得封闭形式的距离比。接下来,为了从距离比估计中获得源位置,我们提出了一个两步线性最小二乘 (TLLS) 估计器,它利用了源坐标和范围变量之间的已知关系。最后,我们提出了一个最大后验(MAP)估计器,它通过最大化 DRSS 值的后验似然来联合估计源位置和 PLE,给定 PLE 的分布。数值研究验证了所提出的估计器相对于最先进技术的改进定位精度。
更新日期:2021-01-01
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