Ground Alignment Metrics for Inertial Sensor Bias Drift Estimation
Static ground alignment accuracy depends on isolating Earth rotation rate from sensor bias instability through multi-position indexing and Allan variance metrics.
Allan Variance Analysis
Variance Slope
Time-series statistical processing computes cluster variances across expanding averaging times to separate stationary white noise from non-stationary drift in frequency standards and inertial sensors. Practiced across sensor characterization, allan variance analysis converts raw time-domain output into a logarithmic plot of variance against cluster duration. Quantified slope regions identify specific noise processes including angle random walk and rate random walk without requiring prior filter modeling.
High-rate sampling captures high-frequency quantization noise at the extreme left of the curve while long observation windows expose bias instability and random walk at the right edge.
Noise Separation
Physical noise mechanisms generate distinct logarithmic gradients on the resulting response curve. Angular random walk appears as a negative half slope at short averaging intervals where thermal velocity noise dominates sensor output. Flicker noise forms a zero-slope plateau that defines the underlying instability floor.
Integration Limit
Sensor stability reaches a lower bound where flicker noise yields to long-term drift. Extending integration time past this minimum increases total measurement error.
Verification Protocol
Calibration laboratories record output data under static thermal conditions for durations exceeding twenty-four hours. Test procedures mandate fixed sample rates and uninterrupted logging to prevent spectral leakage across cluster intervals. Instrument compliance requires that extracted coefficients for velocity random walk and bias instability stay beneath maximum specified bounds across the full temperature range.