scholarly journals Advanced Particle Filter Methods

2017 ◽  
Author(s):  
Roi Yozevitch ◽  
Boaz Ben-Moshe
Keyword(s):  
Particle Filter ◽  
Filter Methods

scholarly journals Implicit Equal-Weights Variational Particle Smoother

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 338
Author(s):  
Pinqiang Wang ◽  
Mengbin Zhu ◽  
Yan Chen ◽  
Weimin Zhang
Keyword(s):  
Particle Filter ◽  
Weather Prediction ◽  
Ensemble Methods ◽  
Great Success ◽  
Ensemble Spread ◽  
Ensemble Kalman Filters ◽  
Error Covariance ◽  
Filter Methods ◽  
Geophysical Application ◽  
Prediction Systems

Under the motivation of the great success of four-dimensional variational (4D-Var) data assimilation methods and the advantages of ensemble methods (e.g., Ensemble Kalman Filters and Particle Filters) in numerical weather prediction systems, we introduce the implicit equal-weights particle filter scheme in the weak constraint 4D-Var framework which avoids the filter degeneracy through implicit sampling in high-dimensional situations. The new variational particle smoother (varPS) method has been tested and explored using the Lorenz96 model with dimensions N x = 40 , N x = 100 , N x = 250 , and N x = 400 . The results show that the new varPS method does not suffer from the curse of dimensionality by construction and the root mean square error (RMSE) in the new varPS is comparable with the ensemble 4D-Var method. As a combination of the implicit equal-weights particle filter and weak constraint 4D-Var, the new method improves the RMSE compared with the implicit equal-weights particle filter and LETKF (local ensemble transformed Kalman filter) methods and enlarges the ensemble spread compared with ensemble 4D-Var scheme. To overcome the difficulty of the implicit equal-weights particle filter in real geophysical application, the posterior error covariance matrix is estimated using a limited ensemble and can be calculated in parallel. In general, this new varPS performs slightly better in ensemble quality (the balance between the RMSE and ensemble spread) than the ensemble 4D-Var and has the potential to be applied into real geophysical systems.

scholarly journals Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods

2017 ◽  
Vol 550 ◽  
pp. 412-426 ◽  
Author(s):  
Y.R. Fan ◽  
G.H. Huang ◽  
B.W. Baetz ◽  
Y.P. Li ◽  
K. Huang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Particle Filter ◽  
Ensemble Kalman Filter ◽  
Hydrological Data ◽  
Filter Methods ◽  
Integrated Approaches

Particle Filter Methods for Space Object Tracking

2014 ◽  
Author(s):  
James S. McCabe ◽  
Kyle J. DeMars
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Space Object ◽  
Filter Methods ◽  
Space Object Tracking

scholarly journals Stratified Particle Filter Monocular SLAM

2021 ◽  
Vol 13 (16) ◽  
pp. 3233
Author(s):  
Pawel Slowak ◽  
Piotr Kaniewski
Keyword(s):  
Computational Complexity ◽  
Particle Filter ◽  
Real World ◽  
Real World Data ◽  
Localization And Mapping ◽  
Filter Methods ◽  
Monocular Slam ◽  
Importance Factor ◽  
Sample Weighting ◽  
Number Of Particles

This paper presents a solution to the problem of simultaneous localization and mapping (SLAM), developed from a particle filter, utilizing a monocular camera as its main sensor. It implements a novel sample-weighting idea, based on the of sorting of particles into sets and separating those sets with an importance-factor offset. The grouping criteria for samples is the number of landmarks correctly matched by a given particle. This results in the stratification of samples and amplifies weighted differences. The proposed system is designed for a UAV, navigating outdoors, with a downward-pointed camera. To evaluate the proposed method, it is compared with different samples-weighting approaches, using simulated and real-world data. The conducted experiments show that the developed SLAM solution is more accurate and robust than other particle-filter methods, as it allows the employment of a smaller number of particles, lowering the overall computational complexity.

809 Object Tracking by Pan-tilt Cameras and Arm Robot Using Sparse Template Matching and Particle Filter Methods

2012 ◽  
Vol 2012.50 (0) ◽  
pp. 80901-80902
Author(s):  
Masayuki KAWANAMI ◽  
Hiroyuki UKIDA ◽  
Yasuhiro TERAMA
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Template Matching ◽  
Filter Methods

scholarly journals Simplified partial resampling method for state estimation usingparticle filter

2018 ◽  
Vol 7 (2.7) ◽  
pp. 243
Author(s):  
M Tirumala Reddy ◽  
Y Sri Ganesh ◽  
Ch Lakshmi Gayathri ◽  
T Megha Shyam ◽  
S Koteswar Rao ◽  
...  
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Gaussian Noise ◽  
The State ◽  
Resampling Methods ◽  
Simple Pendulum ◽  
Filter Methods ◽  
Resampling Method ◽  
Paper Work ◽  
Non Gaussian

Particle filter methods are used in the estimation and tracking of the objects for non-linear and non-gaussian noise conditions. In this paper work the object estimation using partial resampling methods are discussed. On using partial resampling method resampling becomes faster. The performance of particle filter with partial resampling scheme is analyzed using the state estima-tion of a simple pendulum.

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