基于变分贝叶斯学习的无线传感器网络中距离相关噪声下的联合定位与路径损耗指数估计
Variational Bayesian Learning based Joint Localization and Path Loss Exponent with Distance-dependent Noise in Wireless Sensor Network
AI总结：
针对距离相关噪声下的无线传感器网络，提出基于变分贝叶斯学习的联合定位与路径损耗指数估计框架，通过近似后验分布实现联合优化，提供迭代闭式解，性能接近BCRB且优于多数现有算法。
摘要：
本文聚焦于在距离相关噪声下联合优化位置和路径损耗指数（PLE）的挑战。不同于定位和路径损耗指数估计问题中使用的传统独立噪声模型，我们考虑了一个更现实的模型，该模型结合了距离相关的噪声方差，正如最近的理论分析和实验结果所揭示的那样。距离相关噪声引入了一个具有未知噪声功率和PLE的复杂噪声模型，导致一个异常具有挑战性的非凸和非线性优化问题。在本研究中，我们解决了一个联合定位和路径损耗指数估计问题，该问题涵盖距离相关噪声、未知参数以及传感器节点位置的不确定性。为了克服问题中固有的难以处理的非线性和非凸目标函数，我们引入了一个基于变分贝叶斯学习的框架，该框架通过利用对真实后验分布的有效近似，实现了定位、路径损耗指数和参考噪声参数的联合优化。此外，所提出的联合学习算法提供了迭代闭式解，并且在计算复杂度方面表现出优于现有算法的性能。计算机仿真结果表明，所提出的算法接近贝叶斯克拉美-罗下界（BCRB）的性能，在某些情况下实现了与（最大似然-高斯消息传递）ML-GMP算法相当的定位性能，并且在所有情况下都优于其他比较算法。
英文摘要：
This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distance-dependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoretical analyses and experimental results. The distance-dependent noise introduces a complex noise model with unknown noise power and PLE, resulting in an exceptionally challenging non-convex and nonlinear optimization problem. In this study, we address a joint localization and path loss exponent estimation problem encompassing distance-dependent noise, unknown parameters, and uncertainties in sensor node locations. To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution. Furthermore, the proposed joint learning algorithm provides an iterative closed-form solution and exhibits superior performance in terms of computational complexity compared to existing algorithms. Computer simulation results demonstrate that the proposed algorithm approaches the performance of the Bayesian Cramer-Rao bound (BCRB), achieves localization performance comparable to the (maximum likelihood-Gaussian message passing) ML-GMP algorithm in some cases, and outperforms the other comparison algorithm in all cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
