So sánh phương pháp

	Bộ lọc hạt (Monte Carlo tuần tự)	Bộ lọc Kalman
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	1993	1960
Người khởi xướng	Gordon, Salmond & Smith	Rudolf E. Kalman
Loại	Sequential Monte Carlo estimator	recursive Bayesian filter
Công trình gốc	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.	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Tên gọi khác	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Liên quan	4	5
Tóm tắt	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.	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.