Sensor fusion

Filters

There are 3 notable steps that are discussed below.

Intialization

Initialize the filter by giving it a belief in the state to work with.

Prediction

This is also knows as system propagation. It uses the system model (literally what it sounds like, a model of the system) to form a new prediction.

Update

Measure and find residuel (with the prediction from previous step) and use it to generate an estimate. This estimate would be incorporating the information from both the actual measurement and the prediction made using the system model.

Bayesian filters

Prior

The probability before incorporating measurements is called the prior.

Likelihood

Once we make a measurement, and know how accurate the sensor is, we scale up the prior (which is a pdf) based on what our measurement tells us.
Since it is scaled, likelihood is not a pdf.

Posterior

A normalized likelihood, which is a pdf, is called the posterior. It is the probability distribution after we take our measurement into account.