Multi-Sensor Fuzzy Data Fusion Using Sensors with Different Characteristics

TL;DR

This paper introduces a fuzzy data fusion method that combines raw sensor data considering their unique accuracy and frequency response characteristics, enhancing control system output estimation.

Contribution

It presents a novel rule-based fuzzy system for multi-sensor data fusion that accounts for sensors' differing characteristics, improving data integration efficiency.

Findings

Enhanced accuracy in control system output estimation

Effective fusion of sensors with complementary characteristics

Simulation results demonstrate improved performance

Abstract

This paper proposes a new approach to multi-sensor data fusion. It suggests that aggregation of data from multiple sensors can be done more efficiently when we consider information about sensors' different characteristics. Similar to most research on effective sensors' characteristics, especially in control systems, our focus is on sensors' accuracy and frequency response. A rule-based fuzzy system is presented for fusion of raw data obtained from the sensors that have complement characteristics in accuracy and bandwidth. Furthermore, a fuzzy predictor system is suggested aiming for extreme accuracy which is a common need in highly sensitive applications. Advantages of our proposed sensor fusion system are shown by simulation of a control system utilizing the fusion system for output estimation.