It is possible to implement importance sampling, and particle filter algorithms, where the importance sampling weight is random. Such random-weight algorithms have been shown to be efficient for ...

As the final course in the Applied Kalman Filtering specialization, you will learn how to develop the particle filter for solving strongly nonlinear state-estimation problems. You will learn about the ...

The discovery of particle filtering methods has enabled the use of nonlinear filtering in a wide array of applications. Unfortunately, the approximation error of ...

An innovative algorithm for detecting collisions of high-speed particles within nuclear fusion reactors has been developed, inspired by technologies used to determine whether bullets hit targets in ...