Application of Particle Filter Algorithm Based on Gaussian Clustering in Dynamic Target Tracking

2019 ◽  
Vol 29 (3) ◽  
pp. 559-564
Author(s):  
Kai Yang ◽  
Jun Wang ◽  
Zhengwen Shen ◽  
Zaiyu Pan ◽  
Wenhui Yu
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Dynamic Target ◽  
Particle Filter Algorithm

Niche PSO Particle Filter with Particles Fusion for Target Tracking

2012 ◽  
Vol 239-240 ◽  
pp. 1368-1372
Author(s):  
Hai Tao Yao ◽  
Hai Qiang Chen ◽  
Tuan Fa Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Target Tracking ◽  
Video Sequence ◽  
Population Distribution ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Weight Calculation ◽  
Dynamic Target ◽  
Particle Filter Algorithm

An improved particle filter algorithm is proposed to track a randomly moving target in video. In particle filter framework, a particle swarm optimization improved by niche technique which implemented by restricted competition selection is integrated. It can move particles into high likelihood area of target and form multi-population distribution, so that the searching capability of particles is enhanced and then the adaptation to the change of dynamic target state is improved. The particles of niching particle swarm optimization and the particles of particle filter are integrated for new particle weight calculation and finally realize a new particle filter for target tracking in video sequence.

scholarly journals A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

2021 ◽  
Vol 11 (21) ◽  
pp. 10270
Author(s):  
Yong Tao ◽  
Fan Ren ◽  
He Gao ◽  
Tianmiao Wang ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Particle Filtering ◽  
Servo Control ◽  
Simulation Environment ◽  
Visual Servo ◽  
Affine Groups ◽  
Actual Environment ◽  
Dynamic Target ◽  
Particle Filter Algorithm

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.

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

Target Tracking Based on Optimized Particle Filter Algorithm

2013 ◽  
Vol 8 (5) ◽  
Author(s):  
Junying Meng ◽  
Jiaomin Liu ◽  
Juan Wang ◽  
Ming Han
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Particle Filter Algorithm

Multi-Target Tracking Based on Optimized Particle Filter Algorithm

2012 ◽  
Vol 11 (1) ◽  
pp. 630-633
Author(s):  
Junying Meng ◽  
Jiaomin Liu ◽  
Juan Wang ◽  
Ming Han
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Multi Target Tracking ◽  
Particle Filter Algorithm

scholarly journals Improved Interacting Multiple Model Particle Filter Algorithm

2018 ◽  
Vol 36 (1) ◽  
pp. 169-175 ◽  
Author(s):  
Qiaoran Liu ◽  
Xun Yang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Model Particle ◽  
Interactive Multiple Model ◽  
Particle Filter Algorithm

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

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