Comparar métodos

	Filtro de Kalman	Filtro de Partículas (Monte Carlo Secuencial)
Campo	Bayesiano	Bayesiano
Familia	Bayesian methods	Bayesian methods
Año de origen	1960	1993
Autor original	Rudolf E. Kalman	Gordon, Salmond & Smith
Tipo	recursive Bayesian filter	Sequential Monte Carlo estimator
Fuente seminal	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.
Alias	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Relacionados	5	4
Resumen	The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.	The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.