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Most of the modern intelligent mobile devices such as intelligent vehicles or robots rely on sensor fusion to perceive the environment and make the decision on direction by traditional Maximum Likelihood (ML) criterion and possible direct decision feedback. To optimally fuse the sensor observation, the authors propose a novel approach called Decision-Prediction fusion (DP fusion). It further includes the previous decision as well as the previous state in the state transition concept of Kalman filter to derive the a prior probability of the current state. Thus traditional sensor ML fusion is converted to Maximum A posteriori Probability (MAP) detection by this approach.
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