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雷达卡
摘要翻译：
研究了无线网络中的分布式协作定位问题，即没有先验位置知识的节点（Agent）希望确定自己的位置。在非合作方法中，定位仅基于已知位置的参考节点（锚）的信息。然而，在合作定位中，来自其他代理的信息也被考虑在内。合作定位要求对代理位置的不确定性进行编码。为了满足这一需求，我们采用随机推理来进行定位，这种推理内在地考虑了Agent位置的不确定性。然而，随机推理是以计算和信息交换方面的高成本为代价的。为了放松对推理算法的要求，我们提出了位置约束随机推理框架，首先将节点的位置限制在可行集内。我们使用凸多边形对智能体的可能位置施加约束。这样，我们使得推理算法能够集中于样本空间的重要区域，而不是整个样本空间。仿真结果表明，与目前最先进的无约束推理算法相比，该算法具有更高的定位精度、更低的计算复杂度和更快的收敛速度。
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英文标题：
《Position-Constrained Stochastic Inference for Cooperative Indoor
Localization》
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作者：
Rico Mendrzik and Gerhard Bauch
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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：
We address the problem of distributed cooperative localization in wireless networks, i.e. nodes without prior position knowledge (agents) wish to determine their own positions. In non-cooperative approaches, positioning is only based on information from reference nodes with known positions (anchors). However, in cooperative positioning, information from other agents is considered as well. Cooperative positioning requires encoding of the uncertainty of agents' positions. To cope with that demand, we employ stochastic inference for localization which inherently considers the position uncertainty of agents. However, stochastic inference comes at the expense of high costs in terms of computation and information exchange. To relax the requirements of inference algorithms, we propose the framework of position-constrained stochastic inference, in which we first confine the positions of nodes to feasible sets. We use convex polygons to impose constraints on the possible positions of agents. By doing so, we enable inference algorithms to concentrate on important regions of the sample space rather than the entire sample space. We show through simulations that increased localization accuracy, reduced computational complexity, and quicker convergence can be achieved when compared to a state-of-the-art non-constrained inference algorithm.
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PDF链接：
https://arxiv.org/pdf/1802.02794
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