
Mathematical Bivariate Polynomial Matrix Fitting for Thermal Drift Compensation
Bivariate polynomial matrix fitting corrects non-linear sensor thermal drift when inputs are normalized and solved via singular value decomposition.

Bivariate polynomial matrix fitting corrects non-linear sensor thermal drift when inputs are normalized and solved via singular value decomposition.

Calibration maps sensor error against traceable standards, while drift stems from physical aging, mechanical strain, and environmental stress over time.

Allan Variance bias stability metrics directly determine discrete Kalman process noise matrix entries to prevent filter divergence under non-stationary drift.
Silicon substrate expansion mismatches create stress across MEMS structures, driving zero-g drift that demands isolated anchors and hysteresis modeling.
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