Improving Measurement Accuracy With a Neuro-Inspired Multi-Sensor Approach

Abstract

In this paper, we present a novel approach that draws inspiration from the way the brain processes sensory information, using multiple sensors to provide redundant and complementary information that can be combined with machine learning techniques to improve accuracy and reduce noise. In particular, we train a machine learning model to estimate ground truth signals using response data obtained from multiple sensors exhibiting heterogeneity. After only one stage of training, our method can be applied under various conditions. We present simulation results demonstrating the effectiveness of our approach in reducing noise and improving accuracy in a variety of measurement scenarios. Our method achieves competitive outcomes in comparison to the Kalman filter without relying on historical data. The theoretical efficacy of our method is elucidated by establishing a connection with parallel Gaussian channels from information theory. Moreover, we provide estimation to the extent of performance improvement in relation to the increasing count of sensors. Our approach has the potential to be applied to a wide range of industries and fields.INDEX TERMS Machine learning, multi-sensor approach, neuro-inspired, parallel Gaussian channels.YUN WANG received the B.S. and M.