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Show HN: Automated smooth Nth order derivatives of noisy data

(github.com)
146 points hugohadfield | 1 comments | 16 Oct 24 20:17 UTC | HN request time: 0.193s | source

This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to try it out on! If people find kalmangrad useful I would be more than happy to add a few more features etc. and I would be very grateful if people sent in any bugs they spot.. Thanks!
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pm ◴[16 Oct 24 21:51 UTC] No.41864206[source]▶
Congratulations! Pardon my ignorance, as my understanding of mathematics at this level is beyond rusty, but what are the applications of this kind of functionality?
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1. auxym ◴[17 Oct 24 19:28 UTC] No.41872941[source]▶
My first thought as a mechanical engineer is whether this could be useful for PID controllers. Getting a usable derivative value for the "D" term is often a challenge because relatively small noise can create large variations in the derivative, and many filtering methods (eg simple first-order lowpass) introduce a delay/phase shift, which reduces controller performance.