The speed of a wheeled vehicle is usually estimated using wheel speed sensors (WSS) or GPS. If these signals are unavailable, other methods must be used. We propose a novel approach exploiting the fact that vibrations from rotating axles, with fundamental frequency proportional to vehicle speed, are transmitted via the vehicle chassis. Using an accelerometer, these vibrations can be tracked to estimate vehicle speed while other sources of vibrations act as disturbances. A state-space model for the dynamics of the harmonics is presented and formulated such that there is a conditional linear-Gaussian substructure, enabling efficient Rao-Blackwellized methods. A variant of the Rao-Blackwellized point-mass filter is derived, significantly reducing computational complexity, and reducing the memory requirements from quadratic to linear in the number of grid points. It is applied to experimental data from the sensor cluster of a car and validated using the rotational frequency from WSS data. The proposed method shows improved performance and robustness in comparison to a Rao-Blackwellized particle filter implementation and a frequency spectrum maximization method.
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