Determinant-based Fast Greedy Sensor Selection Algorithm

TL;DR

This paper introduces a determinant-based greedy sensor selection algorithm that improves estimation accuracy and computational efficiency for sensor placement problems, especially in oversampling scenarios, using a unified approach and real datasets.

Contribution

The paper extends previous sensor selection methods by developing a unified determinant maximization algorithm applicable to both undersampling and oversampling cases, with proven bounds and real-world validation.

Findings

Improves estimation error by ~10% over conventional methods.

Selects sensors in seconds for large datasets, outperforming hours needed by other algorithms.

Effective in real-world datasets like NOAA-SST with over ten thousand candidate points.

Abstract

In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach. The procedure for the maximization of the determinant of the corresponding matrix is proved to be mathematically the same as that of the previously proposed QR method when the number of sensors is less than that of state variables (undersampling). On the other hand, the authors have developed a new algorithm for when the number of sensors is greater than that of state variables (oversampling). Then, a unified formulation of the two algorithms is derived, and the lower bound of the objective function given by this algorithm is shown using the monotone submodularity of the objective function. The effectiveness of the proposed algorithm on the problem using real datasets is demonstrated by comparing with the results of other algorithms. The numerical results show that the proposed algorithm improves the estimation error by approximately 10% compared with the conventional methods in the oversampling case, where the estimation error is defined as the ratio of the difference between the reconstructed data and the full observation data to the full observation. For the NOAA-SST sensor problem, which has more than ten thousand sensor candidate points, the proposed algorithm selects the sensor positions in few seconds, which required several hours with the other algorithms in the oversampling case on a 3.40 GHz computer.