←back to thread

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: 0.001s | 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!
Show context
Animats ◴[16 Oct 24 23:04 UTC] No.41864781[source]▶
That's useful. Can it generate a simple filter for later real-time use, based on the statistics of the noise? That would be useful for self-tuning controllers.
replies(1): >>41868274 #
1. hugohadfield ◴[17 Oct 24 10:43 UTC] No.41868274[source]▶
Glad you like it! This library will not generate a set of convolutional filter coefficients for you if that is what you are after, I'm sure it would be possible to do some fairly nasty maths to get out some kind of equivalent convolutional kernal for a given tuning, or you could wrap an optimiser round it and try to walk your coefficients to something equivalent. I would say though that the juice would almost certainly not be worth the squeeze. The kalman filter is easily lightweight enough to run in real time itself (it was developed for this task), I've deployed several in real time embedded scenarios on a range of platforms (inc. microcontrollers) and it also has the added advantage of doing handling jitter in input timing etc.