Particle filter object tracking

1. What is particle filter tracking?
2. Is a particle filter a Kalman filter?
3. Is particle filter a Bayes filter?
4. What are particles in particle filter?

What is particle filter tracking?

The trackingPF object represents an object tracker that follows a nonlinear motion model or that is measured by a nonlinear measurement model. The filter uses a set of discrete particles to approximate the posterior distribution of the state. The particle filter can be applied to arbitrary nonlinear system models.

Is a particle filter a Kalman filter?

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.

Is particle filter a Bayes filter?

Bayes Filtering is the general term used to discuss the method of using a predict/update cycle to estimate the state of a dynamical system from sensor measurements. As mentioned, two types of Bayes Filters are Kalman filters and particle filters.

What are particles in particle filter?

Particle filtering is based on recursive Bayesian filtering with Monte Carlo simulations. The method approximates the Bayesian posterior PDF with a set of randomly chosen weighted samples. Each sample of the state vector is referred to as a particle.