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.
Polynomial Quantization Noise
Signal Distortion
Error occurs when a continuous signal is mapped to a discrete set of values using a non-linear or higher order function. The presence of polynomial quantization noise indicates that the rounding error is correlated with the input amplitude or its derivatives. Digital to analog converters used in high fidelity audio and precision instrumentation must minimize these artifacts.
Spectrum Characteristic
Traditional white noise is distributed evenly, but these specific errors often appear as harmonic spikes in the frequency domain. This clustering makes the noise easier to hear or detect with automated analysis tools. Calculation of the signal to noise ratio must account for these non-linearities.
Mitigation Method
Dithering involves adding a small amount of random noise to the signal before quantization. This technique breaks the correlation between the error and the input, turning the harmonic distortion into a less intrusive background hiss. Advanced algorithms use noise shaping to move the error energy into frequency bands that are outside the range of interest.
High resolution converters with more bits also reduce the magnitude of the initial rounding step. This approach is standard in medical imaging and satellite communication.
Measurement Floor
Effective bits of a system describe the actual performance in the presence of these errors. Laboratory verification uses sine wave testing to map the distortion profile.