Blind Robust Estimation With Missing Data for Smart Sensors Using UFIR Filtering
Blind Robust Estimation With Missing Data for Smart Sensors Using UFIR Filtering
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DOI：
10.1109/jsen.2017.2654306
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发表时间：
2017-03
影响因子：
4.3
通讯作者：
Miguel Vazquez-Olguin;Y. Shmaliy;C. Ahn;O. Ibarra-Manzano
中科院分区：
文献类型：
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作者：
Miguel Vazquez-Olguin;Y. Shmaliy;C. Ahn;O. Ibarra-Manzano
智能传感器通常被设计成在噪声和数据缺失信息不完整的恶劣工业条件下工作。因此，要求信号处理算法是无偏的、稳健的、可预测的和理想的盲算法。在本文中，我们提出了一种新的盲迭代无偏有限脉冲响应(UFIR)滤波算法，它满足了这些要求，是一种比卡尔曼滤波(KF)更稳健的选择。分析了UFIR滤波器和KF之间的稳健性折衷。开发了预测UFIR算法，以在临时丢失数据的情况下在控制回路中运行。对监测城市和工业环境所需的一氧化碳浓度和温度测量进行了实验验证。在短时间内和长基线上证明了预测UFIR估计器的高精度和高精度。
Smart sensors are often designed to operate under harsh industrial conditions with incomplete information about noise and missing data. Therefore, signal processing algorithms are required to be unbiased, robust, predictive, and desirably blind. In this paper, we propose a novel blind iterative unbiased finite impulse response (UFIR) filtering algorithm, which fits these requirements as a more robust alternative to the Kalman filter (KF). The tradeoff in robustness between the UFIR filter and KF is learned analytically. The predictive UFIR algorithm is developed to operate in control loops under temporary missing data. Experimental verification is given for carbon monoxide concentration and temperature measurements required to monitor urban and industrial environments. High accuracy and precision of the predictive UFIR estimator are demonstrated in a short time and on a long baseline.