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Sensor fusion is a signal processing discipline wherein one tries to merge - or fuse - sensor data information coming from different physical sensors. In this paper we illustrate how sensor fusion can be used to design a low tire pressure warning system using existing sensors on a modern vehicle. Results from real-world tests on a car are given as illustration of the method's feasibility.

The idea of a virtual sensor is to extract information of parameters that cannot be measured directly, or at least would require very costly sensors, by only using available information. Virtual sensors are described for the friction between road and tire, the tire inflation pressure and wheel imbalance. There are certain interconnections between these virtual sensors so they are preferably implemented in one unit. Results from a real-time implementation, using mainly sensor information from the CAN bus, are given.

In the presented sensor fusion approach, centralized filtering of related sensor signals is used to improve and correct low price sensor measurements. From this, we compute high-quality state information as drift-free yaw rate and exact velocity (accounting for unknown tire radius and slipping wheels on 4WD vehicles). The basic tool here is a Kalman filter supported by change detection for sensor diagnosis. Results and experience of real-time implementations are presented.

In this paper we use the idea of parameterizing the ionization current using the sum of two Gaussian functions in an indirect scheme to estimate the AFR. In the first step of the scheme, the Gaussian functions are fitted to the ion signal using a standard least-squares fit. Then, as a second step, the AFR is estimated using the six parameters of the Gaussian functions plus the ignition angle and measurements of the engine speed. The experimental tests show that it is possible to estimate the AFR with good accuracy, using this approach. The best results were obtained using a neural network approach and it is shown in the paper that the AFR can be estimated from the ionization current down to approximately 0.1% in mean square error.