Small functions for implementing Kalman Filters in Python and C++.
def gaussian_fn(mu, sigma_sq, x)
Bayes rule, multiplication. Updates the state estimate and its uncertainty
Total probability, addition/ convolution
Kalman Filter states (variables) can be divided in observable and hidden. Multiple instances of an observable variable allows us to make inferences about a hidden variable, which cannot be observed directly.
New location is equivalent to old location plus velocity:
x' = x + Δt * ẋ'