So sánh phương pháp

	Bộ lọc Kalman Phân cấp	Bộ lọc hạt (Monte Carlo tuần tự)
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	1994	1993
Người khởi xướng	Chou, Willsky & Benveniste	Gordon, Salmond & Smith
Loại	recursive Bayesian state estimator	Sequential Monte Carlo estimator
Công trình gốc	Chou, K. C., Willsky, A. S., & Benveniste, A. (1994). Multiscale recursive estimation, data fusion, and regularization. IEEE Transactions on Automatic Control, 39(3), 464–478.	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.
Tên gọi khác	multi-scale Kalman filter, multilevel Kalman filter, hierarchical state-space filter, HKF	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Liên quan	4	4
Tóm tắt	The Hierarchical Kalman Filter (HKF) extends the classic Kalman filter to systems with multiple levels or scales of state representation. It applies Kalman recursions at each level of a hierarchy — from coarse to fine resolution or from global to local subsystems — and passes information across levels via upward and downward sweeps, producing optimal linear state estimates throughout a structured state-space.	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.