论文标题
基于数据驱动的传感器选择方法，基于对高维数据的近端优化，具有相关的测量噪声
Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
论文作者
论文摘要
本文提出了一种数据驱动的传感器选择方法，用于具有强相关测量噪声的高维非动力系统。所提出的方法基于近端优化，并通过最大程度地减少Fisher Information矩阵矩阵的轨迹来确定传感器位置。所提出的方法可以避免使用密切相关的测量噪声的传感器选择难度，在这种噪声中，必须事先知道可能的传感器位置以计算选择传感器位置的精确矩阵。该问题可以通过乘数的交替方向方法有效地解决，并且所提出的方法的计算复杂性与电位传感器位置的数量成正比，与测量噪声模型的低级别表达相结合时。通过使用人工数据集的实验证明了所提出的方法比现有传感器选择方法的优点。
The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based on proximal optimization and determines sensor locations by minimizing the trace of the inverse of the Fisher information matrix under a block-sparsity hard constraint. The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations. The problem can be efficiently solved by the alternating direction method of multipliers, and the computational complexity of the proposed method is proportional to the number of potential sensor locations when it is used in combination with a low-rank expression of the measurement noise model. The advantage of the proposed method over existing sensor selection methods is demonstrated through experiments using artificial and real datasets.
