摘要翻译：
本文给出了Kalman滤波递推L_1极小化的一些新结果。我们把L_1范数看作一个显式约束，表示为待估计状态的非线性观测。将待估计的稀疏向量解释为从错误（欠采样）测量中观察到的状态，我们可以解决时间和空间变化的稀疏性，任何一种先验信息，也可以很容易地解决现有测量中的非平稳误差影响。本质上，在我们的方法中，我们略微偏离了经典的基于RIP的方法，转向对零空间结构的更直观的理解，这与估计理论中确定性和随机可观测性的工程概念密切相关
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英文标题：
《Some New Results on l1-Minimizing Nullspace Kalman Filtering for Remote
  Sensing Applications》
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作者：
Otmar Loffeld (Center for Sensorsystems, University of Siegen), Dunja
  Alexandra Hage (Center for Sensorsystems, University of Siegen), Miguel
  Heredia Conde (Center for Sensorsystems, University of Siegen), Ling Wang
  (Key Lab. of Radar Imaging and Microwave Photonics, Nanjing University of
  Aeronautics and Astronautics)
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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：
  This paper describes some new results on recursive l_1-minimizing by Kalman filtering. We consider the l_1-norm as an explicit constraint, formulated as a nonlinear observation of the state to be estimated. Interpretiing a sparse vector to be estimated as a state which is observed from erroneous (undersampled) measurements we can address time- and space-variant sparsity, any kind of a priori information and also easily address nonstationary error influences in the measurements available. Inherently in our approach we move slightly away from the classical RIP-based approaches to a more intuitive understanding of the structure of the nullspace which is implicitly related to the well understood engineering concepts of deterministic and stochastic observability in estimation theory
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PDF链接：
https://arxiv.org/pdf/1808.06538