  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.
