Dynamic weighted average in multisensory systems

Implementations of multisensor systems such as sensor array systems and sensor network systems have increased in the last years. This has created the need for algorithms to detect and act against errors and faults in the elements that make up the system. In this work, the particular case of redundant similar-sensor systems is addressed. Here, a new method for dynamically estimating the variance of all sensors in the system is presented. This method is used together with an online weighted average algorithm for estimating and correcting confidence degrees of information coming from sensors in a multisensor system. Precision in the individual variance estimation is related to the number of sensors, making it suitable for applications with a large number of sensors. Also, variance estimator has low computational cost, which permits its implementation on smart devices such as based-MEMS systems.