A generalized look-ahead method for adaptive multiple sequential data fusion and decision making

This paper presents a formulation of look-ahead decision-making algorithm for sensor fusion systems across levels with the I/O mode characterizations of feature in-decision out or data in-decision out methods. The sequential data fusion is conducted through a dynamic behavior modeling method capable of estimating the observed system parameters from the raw sensory measurements over period of time. The temporal estimated model is used for forward prediction of the observed system output for decision-making. A self-evaluation method to estimate the prediction quality is used to generate the individual decision confidence for final decision integration which is conducted through a multi-layered fuzzy linguistic reasoning engine. The validity of the proposed technique is confirmed by experimental results in which we perform sensor fusion of ultrasonic range data and vision detection information for motion decision on target tracking applications. The experimental results demonstrate the feasibility and accuracy of the multisensor fusion method