Adaptive Polynomial Predictive Filter: Solving Inconsistent and Interrupted Sensor Data Challenges

Abstract

This article discusses the challenges faced by sensor fusion systems owing to inconsistent and interrupted sensor data and proposes an adaptive polynomial predictive filter-based sensor fusion approach to address this problem. This approach incorporates an adaptive polynomial filter based on polynomial extrapolation that can effectively capture the temporal dynamics of the sensor data and provide a robust estimate of the underlying signal. This paper provides a detailed analysis of the proposed approach, including its theoretical foundation, implementation, and performance evaluation using simulated sensor data. The simulation results demonstrate that the adaptive polynomial sensor fusion approach outperforms several state-of-the-art sensor fusion techniques, particularly in scenarios in which the sensor data are inconsistent or interrupted. The article concludes that the proposed approach offers an effective solution to the problem of inconsistent and interrupted sensor data in sensor fusion, with potential applications in various fields including robotics, autonomous vehicles, and healthcare.