Optimizing Spatial Sensing Performance with Kriging and SRGAN - A Feasibility Study

Several studies have aimed at improving sensor performance through deep learning. In this paper, we explore the feasibility of using advanced machine learning techniques, specifically Kriging and Super Resolution Generative Adversarial Networks, to enhance the performance and accuracy of simulated force sensitive resistor matrices with a low number of sensors. Kriging is a geostatistical method that uses spatial correlations to interpolate or predict values at unsampled locations, while Super Resolution Generative Adversarial Networks are a type of generative adversarial network that can generate high-resolution images from low-resolution inputs. Our results suggest that machine learning techniques can provide a powerful tool for enhancing the accuracy performance of force sensing technologies, with potential applications in a wide range of fields, especially in autonomous vehicles.