Parralelization of non-linear & non-Gaussian Bayesian state estimators (Particle filters)

2015 ◽  
Author(s):  
Amin Jarrah ◽  
Mohsin M. Jamali ◽  
S. S. S. Hosseini ◽  
Jaakko Astola ◽  
Moncef Gabbouj
Keyword(s):  
Particle Filters ◽  
State Estimators ◽  
Non Linear ◽  
Non Gaussian

scholarly journals Non-Gaussian inference from non-linear and non-Poisson biased distributed data

2014 ◽  
Vol 10 (S306) ◽  
pp. 258-261
Author(s):  
Metin Ata ◽  
Francisco-Shu Kitaura ◽  
Volker Müller
Keyword(s):  
Dark Matter ◽  
Statistical Inference ◽  
Computer Code ◽  
Matter Density ◽  
Density Field ◽  
Distributed Data ◽  
Dark Matter Density ◽  
Posterior Sampling ◽  
Non Linear ◽  
Non Gaussian

AbstractWe study the statistical inference of the cosmological dark matter density field from non-Gaussian, non-linear and non-Poisson biased distributed tracers. We have implemented a Bayesian posterior sampling computer-code solving this problem and tested it with mock data based onN-body simulations.

scholarly journals Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Hui Li ◽  
Yun Liu ◽  
Chuanxu Wang ◽  
Shujun Zhang ◽  
Xuehong Cui
Keyword(s):  
Particle Filters ◽  
A Priori ◽  
Texture Features ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Particle State ◽  
Pedestrian Tracking ◽  
Estimation Problems ◽  
Non Gaussian ◽  
Gaussian Estimation

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.

scholarly journals A surrogate-based approach to nonlinear, non-gaussian joint state-parameter data assimilation

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
John Maclean ◽  
Elaine T. Spiller
Keyword(s):  
Particle Filtering ◽  
Particle Filters ◽  
State Parameter ◽  
High Dimensional ◽  
Simulation Experiments ◽  
Dynamic Mapping ◽  
Dimensional State Space ◽  
Non Gaussian ◽  
Lorenz 96 ◽  
Complementary Strategy

<p style='text-indent:20px;'>Many recent advances in sequential assimilation of data into nonlinear high-dimensional models are modifications to particle filters which employ efficient searches of a high-dimensional state space. In this work, we present a complementary strategy that combines statistical emulators and particle filters. The emulators are used to learn and offer a computationally cheap approximation to the forward dynamic mapping. This emulator-particle filter (Emu-PF) approach requires a modest number of forward-model runs, but yields well-resolved posterior distributions even in non-Gaussian cases. We explore several modifications to the Emu-PF that utilize mechanisms for dimension reduction to efficiently fit the statistical emulator, and present a series of simulation experiments on an atypical Lorenz-96 system to demonstrate their performance. We conclude with a discussion on how the Emu-PF can be paired with modern particle filtering algorithms.</p>

scholarly journals An O(log2N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 342
Author(s):  
Alessandro Varsi ◽  
Simon Maskell ◽  
Paul G. Spirakis
Keyword(s):  
Parallel Computing ◽  
Shared Memory ◽  
Time Complexity ◽  
Distributed Memory ◽  
Particle Filters ◽  
Dynamic Models ◽  
State Of The Art ◽  
Novel Approach ◽  
Non Gaussian ◽  
Memory Architectures

Resampling is a well-known statistical algorithm that is commonly applied in the context of Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian dynamic models. As the models become more complex and accurate, the run-time of PF applications becomes increasingly slow. Parallel computing can help to address this. However, resampling (and, hence, PFs as well) necessarily involves a bottleneck, the redistribution step, which is notoriously challenging to parallelize if using textbook parallel computing techniques. A state-of-the-art redistribution takes O((log2N)2) computations on Distributed Memory (DM) architectures, which most supercomputers adopt, whereas redistribution can be performed in O(log2N) on Shared Memory (SM) architectures, such as GPU or mainstream CPUs. In this paper, we propose a novel parallel redistribution for DM that achieves an O(log2N) time complexity. We also present empirical results that indicate that our novel approach outperforms the O((log2N)2) approach.

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