Data fusion and sensor selection from imperfect sources with regards to the operating environment

Abstract

Purpose – The purpose of this paper is to extend the work of fusing sensors with a Bayesian method to incorporate the sensor's reliability with regard to their operating environment. The results are then to be used with the expected decision formula, conditional entropy and mutual information for suboptimally selecting which types of sensors should be fused where there are operational constraints. Design/methodology/approach – The approach is an extension of previous work incorporating an environment parameter. The expected decision formula then forms the basis for sensor selection. Findings – The author found that the performance of the sensors is correlated to the environment of operation, given that the likelihood of error will be higher in a difficult terrain than would otherwise be the case. However, the author also shows the sensors for fusion will vary if the author knows specifically which terrain the sensors will be operating in. Research limitations/implications – The author notes that in order for this technique to be effective, a proper understanding of the limitations of the sensors, possible terrain types and targets have to be assumed. Practical implications – The practical implication of this work is the ability to assess the performance of fused sensors according to the environment or terrain they might be operating under, thus providing a greater level of sensitivity than would otherwise be the case. Originality/value – The author has extended previous ideas on sensor fusion from imprecise and uncertain sources using a Bayesian technique, as well as developed techniques regarding which sensors should be chosen for fusion given payload or other constraints.