Adaptive recognition and correction of baseline shifts from collocated GPS and accelerometer using two phases Kalman filter

The real-time recognition and precise correction of baseline shifts in strong-motion records is a critical issue for GPS and accelerometer combined processing. This paper proposes a method to adaptively recognize and correct baseline shifts in strong-motion records by utilizing GPS measurements using two phases Kalman filter. By defining four kinds of learning statistics and criteria, the time series of estimated baseline shifts can be divided into four time intervals: initialization, static, transient and permanent. During the time interval in which the transient baseline shift is recognized, the dynamic noise of the Kalman filter system and the length of the baseline shifts estimation window are adaptively adjusted to yield a robust integration solution. The validations from an experimental and real datasets show that acceleration baseline shifts can be precisely recognized and corrected, thus, the combined system adaptively adjusted the estimation strategy to get a more robust solution.