Nonlinear Adaptive Filter for MEMS Gyro Error Cancellation

Thermal biases are the dominate error in low-cost low-power small MEMS gyros. CubeSats often can't afford the power/mass to put a heater on their MEMS gyros and don't have space for a higher-quality gyro. The Nonlinear Adaptive Filters (NAFs) enable precision pointing on CubeSats by eliminating thermal bias (and other) errors. NAFs are also beneficial to larger satellites with higher-quality gyros subjected to rapid thermal changes as NAFs eliminate thermal sensor errors. The Nonlinear adaptive filters (NAF) can learn deterministic gyro errors and cancel the error's effect from attitude estimates. By completely canceling deterministic gyro errors, NAFs improve sensor performance. The project includes: Simulation of NAF (and comparison to state-of-art); Mathematical analysis to optimize NAF tuning; Development of high-fidelity gyro models, especially modeling thermal effects. Milestones include Simulation Development and Thermal Vacuum Testing with several CubeSat-grade gyros.

Nonlinear adaptive filters (NAFs) allow smaller/cheaper/less-accurate gyros perform like higher-accuracy gyros which may provide substantial savings for flight projects with rapid thermal variation like astrophysics observatories or rendezvous spacecraft. Additionally, NAFs improve cubeSat-grade gyro performance, enabling new missions on small platforms.

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