MSE minimization and fault-tolerant data fusion for multi-sensor systems

Abstract

Multi-sensor data fusion is an efficient method to provide both accurate and fault-tolerant sensor readouts. Furthermore, detection of faults in a reasonably short amount of time is crucial for applications dealing with high risks. In order to deliver high accuracies for the sensor measurements, it is required to perform a calibration for each sensor. This paper focuses on designing a fault-tolerant calibrated multisensor system. First, the least squares method is applied to calibrate each sensor using a linear curve fitting function. Next, an analytical technique is proposed to carry out a fault-tolerant multi-sensor data fusion, while minimizing the Mean-Square-Error (MSE) for the final sensor readout. While our data fusion approach is applicable to different multi-sensor systems, the experimental results are shown for 16 temperature sensors, where an environmental thermal chamber was used as the reference model to calibrate the sensors and perform the measurements.