scholarly journals Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

2014 ◽  
Author(s):  
O. Demirel ◽  
W.J. Niessen ◽  
E. Meijering ◽  
I. Smal ◽  
I.F. Sbalzarini
Keyword(s):  
Importance Sampling ◽  
Particle Filtering ◽  
Fast Particle ◽  
Sequential Importance Sampling ◽  
Piecewise Constant

The Improved Particle Filtering Algorithms for Tracking the Signals

2011 ◽  
Vol 403-408 ◽  
pp. 2341-2344
Author(s):  
Xiu Ying Zhao ◽  
Hong Yu Wang ◽  
Shou Yu Tong ◽  
De You Fu
Keyword(s):  
Kalman Filtering ◽  
Particle Filtering ◽  
Sequential Monte Carlo ◽  
Stochastic Methods ◽  
Sequential Importance Sampling ◽  
Robust Tracking ◽  
Random Set ◽  
Filtering Algorithm ◽  
Alternative Hypotheses ◽  
Visual Clutter

The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The PF(Particle Filtering) algorithm uses “sequential importance sampling”, previously applied to the posterior of static signals, in which the probability distribution of possible interpretations is represented by a randomly generated set. PF uses learned “sequential Monte Carlo” models, together with practical observations, to propagate and update the random set over time. The result is highly robust tracking of agile motion. Not withstanding the use of stochastic methods, the algorithm runs in near Real-Time.

A sorted weighting lookahead sampling scheme for accurate and fast particle filtering

2021 ◽  
Author(s):  
Ramakrishna Gurajala ◽  
Praveen B. Choppala ◽  
James Stephen Meka ◽  
Paul D. Teal
Keyword(s):  
Particle Filtering ◽  
Fast Particle ◽  
Sampling Scheme
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