Title
Monte-carlo estimation from observation on stiefel manifold
Abstract
Partial observation of stochastic processes can occur for various reasons, ranging from faulty sensors to occultation issues. In this paper, we consider the problem of estimating the angular velocity of a rotating system from partial observation corrupted by noise. The system is assumed to evolve on the rotation group SO(n), and only k noisy measurements with k <; n are available. We propose an optimal filter to track the angular velocity. We show that, under some conditions, it is possible to recover the angular velocity of the rotating system and we propose a solution based on a Monte-Carlo method (particle filter). In particular, we show that if the angular velocity is stepwise constant, our algorithm succeed in estimating it. Simulations illustrate the proposed approach.
Year
DOI
Venue
2014
10.1109/ICASSP.2014.6854392
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
Monte Carlo methods,angular velocity control,control system analysis,differential geometry,observers,particle filtering (numerical methods),stochastic processes,Monte Carlo estimation,Stiefel manifold observation,angular velocity estimation,angular velocity tracking,noise corrupted partial observation,optimal filter,particle filter,rotating system,stochastic process,Partial observation,Particle filtering,Rotation group,Stiefel manifold,Stochastic process,angular velocity estimation
Mathematical optimization,Monte carlo estimation,Angular velocity,Occultation,Particle filter,Stochastic process,Stiefel manifold,Ranging,Rotation group SO,Mathematics
Conference
ISSN
Citations 
PageRank 
1520-6149
1
0.40
References 
Authors
5
4
Name
Order
Citations
PageRank
Jeremie Boulanger110.73
Nicolas Le Bihan225423.35
Salem Said35912.54
Jonathan H. Manton484371.93