发帖
雷达卡
摘要翻译：
未来的空间任务将受到以下因素的驱动：需要在不降低性能的情况下降低航天器的成本、新的服务和能力，包括可重构性、自主操作、提高分辨率的目标观测和维修（或接近）操作。小型卫星作为传感器网络部署在空间，可以通过星间通信(ISC)实现这些未来目标。在这种小型卫星的分布式网络中开发能够促进ISC的通信子系统需要一系列复杂的设计权衡。对于小型卫星，ISC需要优化的一般设计参数是尺寸、质量、功率和成本（SMaP-C）。实现通信子系统的新颖而有效的设计技术对于构建多个小卫星网络具有沿星间链路实现高数据速率的能力至关重要。在本文中，我们提出了一种在低地球轨道(LEO)部署的小卫星作为传感器网络的ISC中采用射频和激光ISLs的替代方法。对于中短程ISL，我们提出了一种基于LED的可见光通信(VLC)系统，它解决了SMaP的限制，包括实现显著数据速率的能力。我们的研究重点是为皮/纳米级卫星开发物理层，主要考虑太阳背景光照对链路性能的影响。在MATLAB中建立了星间链路(ISL)的解析模型，并对其在不同强度调制和直接检测（IM/DD）方案下的可行性和性能进行了评估。
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英文标题：
《Inter-Satellite Communication System based on Visible Light》
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作者：
David Amanor, and William Edmonson, and Fatemeh Afghah
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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：
Future space missions will be driven by factors such as the need for reduced cost of spacecraft without diminished performance, new services and capabilities including reconfigurability, autonomous operations, target observation with improved resolution and servicing (or proximity) operations. Small satellites, deployed as a sensor network in space, can through inter-satellite communication (ISC) enable the realization of these future goals. Developing the communication subsystem that can facilitate ISC within this distributed network of small satellites require a complex range of design trade-offs. For small satellites, the general design parameters that are to be optimized for ISC are size, mass, and power, as well as cost (SMaP-C). Novel and efficient design techniques for implementing the communication subsystem are crucial for building multiple small satellite networks with capability for achieving significant data-rates along the inter-satellite links (ISLs). In this paper, we propose an alternative approach to RF and laser ISLs for ISC among small satellites deployed as a sensor network in low Earth orbit (LEO). For short to medium range ISLs, we present an LED-based visible light communication (VLC) system that addresses the SMaP constraints, including capability for achieving significant data rates. Our research is focused on the development of the physical layer for pico-/nano class of satellites with prime consideration for the impact of solar background illumination on link performance. We develop an analytical model of the inter-satellite link (ISL) in MATLAB and evaluate its feasibility and performance for different intensity modulation and direct detection (IM/DD) schemes.
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PDF链接：
https://arxiv.org/pdf/1806.01791
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