摘要翻译：
对于线性离散状态空间(LDSS)模型，在一定条件下，线性最小均方滤波估计具有一种方便的递归预测/校正格式，即Kalman滤波(KF)。本文介绍了线性约束最小方差估计(LCMVE)的一般形式，包括线性约束最小方差估计(LCMVE)。因此，LCKF在确定性框架中打开了关于LCMVE的丰富的文献，这些文献可以转换到随机框架中。因此，LCKF可以提供$H_{\\infty}$滤波器和无偏有限脉冲响应滤波器的替代方案，以增强KF的鲁棒性，其性能对系统矩阵中的错误噪声或不确定性敏感
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英文标题：
《Linearly Constrained Kalman Filter For Linear Discrete State-Space
  Models》
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作者：
Eric Chaumette and Francois Vincent
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最新提交年份：
2017
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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：
  For linear discrete state-space (LDSS) models, under certain conditions, the linear least mean squares filter estimate has a convenient recursive predictor/corrector format, aka the Kalman filter (KF). The aim of the paper is to introduce the general form of the linearly constrained KF (LCKF) for LDSS models, which encompasses the linearly constrained minimum variance estimator (LCMVE). Thus the LCKF opens access to the abundant litterature on LCMVE in the deterministic framework which can be transposed to the stochastic framework. Therefore, among other things, the LCKF may provide alternative solutions to $H_{\infty }$ filter and unbiased finite impulse response filter to robustify the KF, which performance are sensible to misspecified noise or uncertainties in the system matrices
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PDF链接：
https://arxiv.org/pdf/1711.01538