An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis

Kang Li, Jinghua Li, Yutao Jiao, Guoru Ding, Shihua Dong
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引用次数: 1

Abstract

RSS-based target localization algorithms are usually derived from channel path-loss model where the measurement noise is generally assumed to obey Gaussian distribution. In this paper, we approximate the realistic measurement noise distribution by a Gaussian mixture model and proposed an improved mixture noise analysis-based RSS target localization algorithm employing expectation maximization, called Gaussian mixture-expectation maximization (GMEM) approach, to estimate target coordinates iteratively, which can be efficiently used for tackling unknown parameters of maximum likelihood estimation and non-convex optimization. Simulations show a considerable performance gain of our proposed localization algorithm in 2-D wireless sensor network.
基于高斯混合噪声分析的RSS目标定位期望最大化解
基于rss的目标定位算法通常由信道路径损耗模型推导而来,该模型通常假设测量噪声服从高斯分布。在这篇文章中,我们近似真实的测量噪声分布的高斯混合模型,提出一种改进的混合噪声分析RSS目标定位算法采用期望最大化,称为高斯mixture-expectation最大化(GMEM)的方法,来估计目标坐标迭代,可以有效地用于解决未知参数的最大似然估计和非凸优化。仿真结果表明,本文提出的定位算法在二维无线传感器网络中具有显著的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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