Abstract: A method for inertial sensor error modeling and compensation comprises obtaining multiple bias drift datasets for an elapsed time period for one or more gyroscopes; generating a 3D bias drift data plot using the multiple bias drift datasets; generating a partial bias drift data image based on the 3D bias drift data plot; and inputting the partial bias drift data image into a machine learning algorithm to predict how bias drift evolves over time for the gyroscopes. The machine learning algorithm uses the partial bias drift data image, the elapsed time period, and temperature history to compute a predicted bias over temperature with respect to time, to thereby predict bias drift data for a future time period for the gyroscopes. The machine learning algorithm outputs a completed bias drift image that represents drift data from the elapsed time period and the predicted bias drift data for the future time period.

Abstract: A method for inertial sensor error modeling and compensation comprises obtaining multiple bias drift datasets for an elapsed time period for one or more gyroscopes; generating a 3D bias drift data plot using the multiple bias drift datasets; generating a partial bias drift data image based on the 3D bias drift data plot; and inputting the partial bias drift data image into a machine learning algorithm to predict how bias drift evolves over time for the gyroscopes. The machine learning algorithm uses the partial bias drift data image, the elapsed time period, and temperature history to compute a predicted bias over temperature with respect to time, to thereby predict bias drift data for a future time period for the gyroscopes. The machine learning algorithm outputs a completed bias drift image that represents drift data from the elapsed time period and the predicted bias drift data for the future time period.

Abstract: A vibratory sensor with electronic balancing is provided. The vibratory sensor includes at least one pair of proof masses, at least one tunable spring having electro-thermodynamic characteristics for each first and second proof mass in the at least one pair of proof masses, and a steering circuit. Each pair of proof masses include a first proof mass and a second proof mass. The first proof mass and the second proof mass can move in opposing directions. Each tunable spring couples an associated one of the first and second proof masses to a substrate. The steering circuit is configured to selectively couple current from a power source to each tunable spring to adjust the stiffness of at least one tunable spring to balance relative movement between the first and second proof masses of the at least one pair of proof masses.

Abstract: Systems and methods for a tuned mass damper in mems resonators are provided. In certain implementations, a system for suppressing undesirable vibration modes comprises a micro-electrical mechanical system (MEMS) resonator, the MEMS resonator comprising two or more proof masses, comprising; and a plurality of anchors, wherein proof masses in the two or more proof masses are connected to the plurality of anchors through a plurality of flexures. The system also further comprises a substrate, wherein the MEMS resonator is mounted to the substrate through the plurality of anchors; and one or more tuned mass dampers configured to counter-act the undesirable vibration modes in the substrate and the MEMS resonator.