A state estimator for rejecting noise and tracking bias in inertial sensors

A state estimation algorithm for rejecting noise and tracking bias in both real-time and post-processing applications of inertial measurement for intermittent motion is presented with experimental results for one-dimensional motion. The algorithm uses characteristics of the sensor noise to automatically recognize motionless periods and update the sensor's bias level without any dependency on application-specific parameters, frequency separation between the signal of interest and the sensor noise, or a high-level system model. Accumulated error in the velocity estimate is eliminated during periods of rest allowing useful velocity estimates to be obtained for arbitrarily long periods given reasonably frequent pauses in the motion. This significantly extends the length of time over which useful position estimates are obtained, especially for postprocessing applications. While derived and demonstrated in the context of inertial measurement, the algorithm can be used to reject noise and/or track bias drift for any signal where zero-level inputs occur intermittently and limits can be set on the noise amplitude, frequency, and bias drift rate.