Therefore, we propose to use fuzzy logic for sensor validation and sensor fusion because no assumption of a Gaussian distribution of the noise need be made. Furthermore, unlike many probabilistic approaches in which the variance of the system perturbation and the noise variables must be known in advance, no such assumptions about the variance are made for the fuzzy approach. Architecture The proposed architecture performs the tasks of sensor validation and sensor fusion. The architecture is depicted in figure 1. Input to this architecture are the raw sensor readings.
Output is a corrected value.
Additional information can be output about which sensor performed in which manner and whether there are indications for failure. A diagnostic module would utilize this information. This means that the predictor will usually lag behind the true state to some degree. On the other hand, outliers are filtered out and a relatively smooth predictor curve is obtained assuming is large enough. To circumvent the lag, a more responsive predictor is desired which can be achieved through making smaller.