Uncompensated Deviation
Unmodeled discrepancies remaining after polynomial compensation algorithms represent the non-deterministic uncertainty bounds of a calibrated instrument. Following calibration corrections, residual error defines the remaining difference between sensor output and the true physical input reference. Higher-order thermal non-linearities and hysteresis effects contribute directly to this leftover uncertainty.
Mathematical compensation models reduce systematic errors but leave small stochastic variations intact. The magnitude of this remaining deviation determines the ultimate achievable accuracy limit.
Error Origin
Hardware hysteresis during thermal cycling creates path-dependent shifts that standard deterministic polynomials cannot predict. Quantization effects in high-resolution analog-to-digital converters contribute additional baseline noise to the residual error profile. Axis misalignment corrections leave tiny non-orthogonal leakage components due to mechanical frame tolerances.
Higher-order non-linearities in sensing elements increase residual discrepancies at scale factor extremes. Precision measurement systems rely on statistical bound models to account for these uncompensated elements during state estimation.
Verification Protocol
Metrology laboratories measure baseline residuals by running automated test sequences across full temperature and dynamic ranges. Residual distributions are evaluated against Gaussian statistical models to confirm that no unmodeled systematic bias remains.
Performance Consequence
Remaining residual error limits the maximum integration period in dead-reckoning navigation systems. Propagation of remaining offset variances increases position uncertainty over prolonged operating times. Qualification certificates document maximum peak-to-peak residual bounds to verify compliance with procurement specifications.