摘要翻译：
本文考虑了一个由搜索和救援应用引起的问题，即无人飞行器(UAV)寻求接近被局部散射体包围的远距离准静止射频(RF)辐射源附近。无人机只利用对接收到的射频信号的多普勒频率的测量，以及它自己的方位，来不断地调整它的轨迹。提出并评估了一种轨迹规划方法，该方法解决了发射器与接收机之间未知载波频率偏移、本地振荡器频率随时间漂移、多普勒方向模糊和观测噪声等技术难题。对于初始轨迹，无人机利用圆周运动估计辐射源的方向，解决了方向模糊问题。然后使用来自通过围绕当前轨迹扰动轴承而获得的频率测量的反馈来连续地调整轨迹。我们证明了该算法收敛于辐射源附近，并通过仿真验证了算法的有效性。
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英文标题：
《RF Source Seeking using Frequency Measurements》
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作者：
Muhammed Faruk Gencel, Upamanyu Madhow, Joao Pedro Hespanha
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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 consider a problem motivated by search-and-rescue applications, where an unmanned aerial vehicle (UAV) seeks to approach the vicinity of a distant quasi-stationary radio frequency (RF) emitter surrounded by local scatterers. The UAV employs only measurements of the Doppler frequency of the received RF signal, along with its own bearing, to continuously adapt its trajectory. We propose and evaluate a trajectory planning approach that addresses technical difficulties such as the unknown carrier frequency offset between the emitter and the UAV's receiver, the frequency drifts of the local oscillators over time, the direction ambiguity in Doppler, and the noise in the observations. For the initial trajectory, the UAV estimates the direction of the emitter using a circular motion, which resolves direction ambiguity. The trajectory is then continuously adapted using feedback from frequency measurements obtained by perturbing the bearing around the current trajectory. We show that the proposed algorithm converges to the vicinity of the emitter, and illustrate its efficacy using simulations.
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PDF链接：
https://arxiv.org/pdf/1803.02494