摘要翻译：
在本文中，我们讨论了一个时变空间场的接收信号强度(RSS)的估计依赖于测量从随机放置和不是非常精确的传感器。我们使用一个路径损耗指数和发射功率未知的无线传播模型，用高斯先验知识，并用经验Bayes方法估计其超参数。我们认为传感器的位置是不完全知道的，这意味着它们代表了模型中的另一个误差源。该传播模型包括阴影，阴影被认为是一个零均值高斯过程，其中两个空间点之间的衰减相关性被量化为点之间距离的指数函数。发射机的位置也是未知的，并通过加权质心方法从数据中估计。我们提出了一个递归贝叶斯方法和众包估计时变RSS场。该方法以高斯过程(GP)为基础，产生空间场的联合分布。此外，它通过保持所需内存的大小有界来总结所有获取的信息。我们还给出了估计参数的贝叶斯克拉姆-拉奥界(BCRB)。最后，我们用合成数据集上的实验结果来说明我们的方法的性能。
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英文标题：
《Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and
  Gaussian Processes》
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作者：
Irene Santos, Juan Jos\'e Murillo-Fuentes, Petar M. Djuri\'c
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In this paper, we address the estimation of a time-varying spatial field of received signal strength (RSS) by relying on measurements from randomly placed and not very accurate sensors. We employ a radio propagation model where the path loss exponent and the transmitted power are unknown with Gaussian priors whose hyper-parameters are estimated by applying the empirical Bayes method. We consider the locations of the sensors to be imperfectly known, which entails that they represent another source of error in the model. The propagation model includes shadowing which is considered to be a zero-mean Gaussian process where the correlation of attenuation between two spatial points is quantified by an exponential function of the distance between the points. The location of the transmitter is also unknown and estimated from the data with a weighted centroid approach. We propose to estimate time-varying RSS fields by a recursive Bayesian method and crowdsourcing. The method is based on Gaussian processes (GP), and it produces the joint distribution of the spatial field. Further, it summarizes all the acquired information by keeping the size of the needed memory bounded. We also present the Bayesian Cram\'er-Rao bound (BCRB) of the estimated parameters. Finally, we illustrate the performance of our method with experimental results on synthetic data sets.
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PDF链接：
https://arxiv.org/pdf/1806.0253