Deep Learning-driven Fast Planning of Informative Sensing for Environmental Field Reconstruction

Informative sensing facilitates the effectiveness and efficiency for environmental monitoring. This paper investigates a deep learning approach that can estimate the optimal results of informative sensing in a random field. First, a Gaussian process (GP) is designated to characterize an environmental field. Then, mutual information over the covariance function of GP is utilized to define the near-optimal locations as the most informative sampling places in the field. At last, a deep neural network is developed to learn the intercorrelation between the covariance function and the the most informative sampling places, which can provide an near online planning of the MI optimization process. In this paper, the experimental results on a real-world dataset validate the proposed learning framework.