Just learnt about Kalman filters from the Udacity self-driving-car course. And was amazed about the ability of the Kalman filters to combine info from multiple sources, in a way that it always decreases the variance.
Which made me think of the old adage 'A man with two clocks is never sure'. But if it were Kalman, he'd be surer than if he had either one of the watches (even if one of them were as precise as an atomic clock and the other was a grandfather clock that needs winding).
I like my bro's version of the Kalman corrected adage: 'A man with one watch is sure about the wrong time'
A good intro to Kalman filters here. Its chapter one of a book
And the wikipedia article.
Current mood: math-y
Currently listening to: Lemongrass - Shankar Tucker
Which made me think of the old adage 'A man with two clocks is never sure'. But if it were Kalman, he'd be surer than if he had either one of the watches (even if one of them were as precise as an atomic clock and the other was a grandfather clock that needs winding).
I like my bro's version of the Kalman corrected adage: 'A man with one watch is sure about the wrong time'
A good intro to Kalman filters here. Its chapter one of a book
And the wikipedia article.
Current mood: math-y
Currently listening to: Lemongrass - Shankar Tucker
