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This is my favourite explanation of how a Kalman filter works http://htmlpreview.github.io/?https://github.com/aguaviva/KalmanFilter/blob/master/KalmanFilter.h

• melling 8y ago
  Here’s a good HN discussion of that article: https://news.ycombinator.com/item?id=16575679 https://news.ycombinator.com/item?id=16575679 HN comments were quite informative too.
  • rdtsc 8y ago
    The main comment there I found made it more complicated for me. That is, saying "it's simple because it is like a bayesian thing". Sometimes raising the level of abstraction helps, but it didn't help me here because now I had to remember all the statistics stuff about bayesian inference.
• rdtsc 8y ago
  Agree. I like how it starts "if you had 2 sensors, here is how you could combine the measurements". Then goes on to say what if you only had 1 sensor, but you know the system evolves in a linear way, you can use that to make a prediction and treat it as the input from a second sensor. That finally made me understand it better, and it's much shorter than most explanations without losing necessary details. Also even though I took both ML and probability and statistics courses saying "it's just Bayesian inference to update the posterior" makes it more confusing for me.
  • vanderZwan 8y ago
    > "it's just Bayesian inference to update the posterior" makes it more confusing for me. I do not know the jargon, but perhaps that is because this sentence does not give you any context for what "the posterior" is referring to?