Modified Algorithms for Approximate MAP Estimation Using Continuous-Time Particle Filter and Edgeworth Series

2020 ◽  
Author(s):  
K. Rybakov
Keyword(s):  
Particle Filter ◽  
Continuous Time ◽  
Map Estimation ◽  
Edgeworth Series

Approximate MMSE and MAP estimation using continuous-time particle filter

2019 ◽  
Author(s):  
Konstantin Chugai ◽  
Ivan Kosachev ◽  
Konstantin Rybakov
Keyword(s):  
Particle Filter ◽  
Continuous Time ◽  
Map Estimation

scholarly journals A Kusuoka–Lyons–Victoir particle filter

2013 ◽  
Vol 469 (2156) ◽  
pp. 20130076 ◽  
Author(s):  
Dan Crisan ◽  
Salvador Ortiz-Latorre
Keyword(s):  
Particle Filter ◽  
Continuous Time ◽  
Nonlinear Filtering ◽  
Computational Effort ◽  
Observation Data ◽  
Minimal Variance ◽  
Discretization Grid ◽  
Filtering Problem ◽  
Grid Mesh ◽  
Number Of Particles

The aim of this paper is to introduce a new numerical algorithm for solving the continuous time nonlinear filtering problem. In particular, we present a particle filter that combines the Kusuoka–Lyons–Victoir (KLV) cubature method on Wiener space to approximate the law of the signal with a minimal variance ‘thinning’ method, called the tree-based branching algorithm (TBBA) to keep the size of the cubature tree constant in time. The novelty of our approach resides in the adaptation of the TBBA algorithm to simultaneously control the computational effort and incorporate the observation data into the system. We provide the rate of convergence of the approximating particle filter in terms of the computational effort (number of particles) and the discretization grid mesh. Finally, we test the performance of the new algorithm on a benchmark problem (the Beneš filter).

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