Particle Filtering
Recently Published Documents


TOTAL DOCUMENTS

1860
(FIVE YEARS 464)

H-INDEX

57
(FIVE YEARS 15)

2021 ◽  
Vol 18 (6) ◽  
pp. 943-953
Author(s):  
Jingquan Zhang ◽  
Dian Wang ◽  
Peng Li ◽  
Shiyu Liu ◽  
Han Yu ◽  
...  

Abstract Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.


Navigation ◽  
2021 ◽  
Vol 68 (4) ◽  
pp. 709-726
Author(s):  
Adyasha Mohanty ◽  
Shubh Gupta ◽  
Grace Xingxin Gao

2021 ◽  
Author(s):  
Hossein Shoushtari ◽  
Cigdem Askar ◽  
Dorian Harder ◽  
Thomas Willemsen ◽  
Harald Sternberg

2021 ◽  
Vol 11 (21) ◽  
pp. 10270
Author(s):  
Yong Tao ◽  
Fan Ren ◽  
He Gao ◽  
Tianmiao Wang ◽  
Shan Jiang ◽  
...  

Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.


Sign in / Sign up

Export Citation Format

Share Document