Kalman filtering: theory and practice
Kalman filtering: theory and practice
Wireless sensor networks: a new regime for time synchronization
ACM SIGCOMM Computer Communication Review
Forward and inverse stochastic filtering for inertial sensor calibration
MIC'06 Proceedings of the 25th IASTED international conference on Modeling, indentification, and control
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
Forward and inverse stochastic filtering for inertial sensor calibration
MIC'06 Proceedings of the 25th IASTED international conference on Modeling, indentification, and control
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Kalman filters are used when data from different sensors is combined to obtain a suboptimal estimation of a dynamic system's state. In most applications, the sensor data enters the filter in two places: some data is fed to the inputs of the dynamic system while other data is used as reference measurements of the system's outputs. In order to yield the best possible estimations, both types of sensors have to be well synchronized, but a hardware synchronization mechanism is not always available. In this paper, the Kalman filter is modified to estimate both the system's state and the time delay between input and output measurements. Simulations show that an accurate software synchronization can be achieved by using this method and that the state estimates improve largely.