Particle Filters (and Navigation)
About this course
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 Monte-Carlo integration and the importance density. You will see how to derive the sequential importance sampling method to estimate the posterior probability density function of a system’s state. You will encounter the degeneracy problem for this method and learn how to solve it via resampling. You will learn how to implement a robust particle-filter in Octave code and will apply it to an indoor-navigation problem.
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What you'll learn
- understand particle filters
- apply Monte Carlo integration
- derive the sequential importance sampling method
- implement a robust particle filter in Octave
- solve the degeneracy problem through resampling
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