A generalized approach for inconsistency detection in data fusion from multiple sensors

This paper presents a sensor fusion strategy based on Bayesian method that can identify the inconsistency in sensor data so that spurious data can be eliminated from the sensor fusion process. The proposed method adds a term to the commonly used Bayesian technique that represents the probabilistic estimate corresponding to the event that the data is not spurious conditioned upon the data and the true state. This term has the effect of increasing the variance of the posterior distribution when data from one of the sensors is inconsistent with respect to the other. The proposed strategy was verified with the help of extensive simulations. The simulations showed that the proposed method was able to identify inconsistency in sensor data and also confirmed that the identification of inconsistency led to a better estimate of desired state variable