For AA273 - State Estimation for Aerospace Systems with Dr. Mac Schwager, I worked with John Lambert to implement an algorithm to track multiple targets given noisy state information, using Markov Chain Monte Carlo sampling, and compared its performance to the more canonical multi-hypothesis Kalman filter. Here’s an example of the algorithm in practice: MCMC_filter

Final Report: MCMC_paper

GitHub Repo: Code is available on GitHub