RMSE vague in 2D particle filtering with LowLevelParticleFilters.jl?

[baggepinnen]

See answer here

Particle Filtering methods

It depends on how large the uncertainty is in the initial distribution compared to the stationary distribution. If you push that example much further, it might converge. You can compute the stationary covariance matrix by solving a Riccati equation with the A jacobian, see Influence of sample rate on performance · LowLevelParticleFilters Documentation for some details or “Asymptotic form” in Kalman filter - Wikipedia using MatrixEquations A = Ajac(...) C = Cjac(...) R_stationary, _ = ared(A, C'…

particle filters add additional monte-carlo approximation error that makes the analysis slightly harder.