Optimal State Estimation for Systems Driven by Jump-Diffusion Process With Application to Road Anomaly Detection

Jump-diffusion processes (JDPs) involve a combination of jumps (Poisson process) and diffusions (Wiener process). JDPs can be used to model large classes of disturbances in engineering applications, such as road disturbances to a car, wind disturbances to an airplane, and system parameter perturbati...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2017-09, Vol.25 (5), p.1634-1643
Main Authors: Zhaojian Li, Kolmanovsky, Ilya V., Kalabic, Uros V., Atkins, Ella M., Jianbo Lu, Filev, Dimitar P.
Format: Article
Language:English
Subjects:
Detectors
Jump–diffusion process (JDP)
Observers
road anomaly detection
Roads
state estimation
Suspensions
Vehicles
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Summary:Jump-diffusion processes (JDPs) involve a combination of jumps (Poisson process) and diffusions (Wiener process). JDPs can be used to model large classes of disturbances in engineering applications, such as road disturbances to a car, wind disturbances to an airplane, and system parameter perturbations. This paper develops a road anomaly detector by exploiting an optimal state estimator for systems driven by JDP in combination with the multi-input observer. State estimation with the JDP-based estimator is shown to have better performance than a Kalman filter when jumps, such as potholes and bumps, are present. The road anomaly detector is implemented in an experimental test vehicle and its experimental validation results are reported.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2016.2620062