Usporedite metode

	Hijerarhijski Kalmanov filtar	Particle Filter (Sequential Monte Carlo)
Područje	Bayesovska statistika	Bayesovska statistika
Obitelj	Bayesian methods	Bayesian methods
Godina nastanka	1994	1993
Tvorac	Chou, Willsky & Benveniste	Gordon, Salmond & Smith
Vrsta	recursive Bayesian state estimator	Sequential Monte Carlo estimator
Temeljni izvor	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.
Drugi nazivi	multi-scale Kalman filter, multilevel Kalman filter, hierarchical state-space filter, HKF	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Srodne	4	4
Sažetak	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.