Howtosignalprocessing
Is particle filter a Bayesian filter?
The particle filter is a Bayesian filter. This means, estimation is performed using Bayesian theory. Bayesian inference allows for estimating a state by combining a statistical model for a measurement (likelihood) with a prior probability using Bayes' theorem.
Is a particle filter a Kalman?
The Kalman and Particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the Particle filter does so by a sequential Monte Carlo method.
How does particle filter work?
In simple terms, the particle filtering method refers to the process of obtaining the state minimum variance distribution by finding a set of random samples propagating in the state space to approximate the probability density function and replacing the integral operation with the sample mean.
