The Anomaly Detection Based on TBM Two-Level Fusion Architecture

The track anomaly detection is the key issue to make sure flying anomaly detected in time for the route flight. Traditional probabilistic frameworks are always based on prior probabilities. Transferable belief model (TBM) theory can generalizes the Bayesian approach without prior probabilities and efficiently deal with heterogeneous data. However, the traditional TBM cannot deal with the discontinuity and uncertainty about the time. Considering the existence of unreliable evidence sources, an alternative anomaly detection method is proposed in the framework of transferable belief model (TBM) theory. A two-level architecture fusion system based on TBM is developed. The novelty of this work is that it can detect both unreliable evidence source and abnormal behavior of the targets within our architecture by using a temporal analysis and a new discounting coefficient through introducing the concept of contribution degrees of features. Detection of abnormal behavior is based on a prediction/observation process and the influence of the faulty sources is weakened through discounting coefficients. The simulations show the better accuracy of decision and precision of time compared with the dynamic evidence reasoning method.