I have a sensor producing bandlimited data at a predictable periodic rate, corrupted by IID white noise (at least over relatively short periods of time). There is also a slowly time-varying bias, which can safely be ignored as it is several orders of magnitude smaller than the white noise.
I want to numerically differentiate the sensor data. The estimate must be causal. Wikipedia has a nice page on the topic along with filter coefficients for deterministic functions.
Is this set of coefficients good in the presence of noise, or is there a better way to perform this estimate? What factors will the new method depend on?
Edit:
Ideally, the method will be computationally cheap as it will be run at a high rate on an embedded platform ...