Kalman Filter | Why Shift the Belief Distribution

1. What is a belief Kalman filter?
2. Is Kalman filter Bayesian?
3. In what sense is the Kalman filter optimal?
4. How to fine tune Kalman filter?

What is a belief Kalman filter?

The Kalman filter is a mathematical technique that provides an efficient recursive means for estimating the states of a process in such a way as to minimize the mean of the squared error.

Is Kalman filter Bayesian?

Kalman filter is the analytical implementation of Bayesian filtering recursions for linear Gaussian state space models. For this model class the filtering density can be tracked in terms of finite-dimensional sufficient statistics which do not grow in time∗.

In what sense is the Kalman filter optimal?

Kalman filters combine two sources of information, the predicted states and noisy measurements, to produce optimal, unbiased estimates of system states. The filter is optimal in the sense that it minimizes the variance in the estimated states.

How to fine tune Kalman filter?

A simple approach to tune a Kalman filter is to use measurements with a known ground truth and adjusting the measurement and process noise of the filter.