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摘要翻译：
本文研究了由K个传感器组成的无线传感器网络(WSN)中具有线性观测模型的高斯向量的分布式估计问题，这些传感器在正交错误无线信道（受衰落和噪声影响）上将其调制量化观测数据发送到一个融合中心，该融合中心估计未知向量。由于网络发射功率有限，每个任务周期只能有一部分传感器处于活动状态。本文提出了网络发射功率约束下最大贝叶斯-费雪信息矩阵(FIM)轨迹的传感器选择和发射功率分配问题，并给出了三种算法。仿真结果表明，与在所有传感器之间均匀分配功率的算法相比，这些算法具有优越性。
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英文标题：
《Sensor Selection and Power Allocation via Maximizing Bayesian Fisher
Information for Distributed Vector Estimation》
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作者：
Mojtaba Shirazi, Alireza Sani, Azadeh Vosoughi
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最新提交年份：
2020
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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 study the problem of distributed estimation of a Gaussian vector with linear observation model in a wireless sensor network (WSN) consisting of K sensors that transmit their modulated quantized observations over orthogonal erroneous wireless channels (subject to fading and noise) to a fusion center, which estimates the unknown vector. Due to limited network transmit power, only a subset of sensors can be active at each task period. Here, we formulate the problem of sensor selection and transmit power allocation that maximizes the trace of Bayesian Fisher Information Matrix (FIM) under network transmit power constraint, and propose three algorithms to solve it. Simulation results demonstrate the superiority of these algorithms compared to the algorithm that uniformly allocates power among all sensors.
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PDF链接：
https://arxiv.org/pdf/1712.00122
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