Robust Autonomous Detection of the Faulty Sensors of a Sensor Array

We propose a technique for the autonomous detection of the faulty sensors of a sensor array that are aberrant relative to the rest. Our approach is based on probabilistically modeling the distribution of the differences between the sensor measurements as a mixture of Gaussians and then classifying further instances of the sensor differences using a naive Bayes classifier. We demonstrate the applicability of this technique to the diagnosis of the sensors/photosites of a CCD array, using sensor array data comprising of randomly selected images. Our technique performs well for different combinations of parameter settings at the detection of the faulty photosites of a CCD array.