Ultra-wideband based dynamic target tracking using Cost-Reference Particle Filtering

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.

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Channel Fault Tolerant Target Tracking in Multi-hop Wireless Sensor Networks Based on Particle Filtering

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2011.00440 ◽

2011 ◽

Vol 37 (4) ◽

pp. 440-448

Author(s):

Xiao-Jun YANG ◽

Ke-Yi XING

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Target Tracking ◽

Particle Filtering ◽

Fault Tolerant ◽

Wireless Sensor

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Dynamic Target Tracking Based on Particle Filter in Actual Environment

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.683.824 ◽

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.

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