Improved particle filtering algorithm based on the multi-feature fusion for small IR target tracking

2011 ◽  
Author(s):  
Er-you Ji ◽  
Guo-hua Gu ◽  
Wei-xian Qian ◽  
Lian-fa Bai ◽  
Xiu-bao Sui
Keyword(s):  
Target Tracking ◽  
Particle Filtering ◽  
Feature Fusion ◽  
Filtering Algorithm

An Improved Particle Filtering Algorithm Based on Mean Shift Algorithm for Infrared Target Tracking

2014 ◽  
Vol 1079-1080 ◽  
pp. 650-653
Author(s):  
Xi Peng Yin ◽  
Lin Lin Xia ◽  
Min Can He ◽  
Wei Cheng
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Posterior Distribution ◽  
Particle Filtering ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
Filtering Algorithm ◽  
Particle Filter Algorithm

Animproved particle filter algorithm which based on mean shift algorithm isintroduced. The algorithm makes the particles move towards the high likelihoodregion in posterior distribution with the effect of mean shift algorithm,increases the efficiency of the particles moving, and reduces the phenomenon ofdegradation and dilution of particles

scholarly journals A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

2021 ◽  
Vol 13 (1) ◽  
pp. 132
Author(s):  
Ning Zhou ◽  
Lawrence Lau ◽  
Ruibin Bai ◽  
Terry Moore
Keyword(s):  
Target Tracking ◽  
Particle Filtering ◽  
Three Dimensional ◽  
Genetic Optimization ◽  
Positioning Accuracy ◽  
Filtering Algorithm ◽  
Resampling Method ◽  
Computation Load ◽  
Crossover And Mutation ◽  
Particle Impoverishment

In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. However, traditional resampling methods cause the problem of particle impoverishment. This problem degrades positioning accuracy and robustness and sometimes may even result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment and improve positioning accuracy, this paper proposes an improved genetic optimization based resampling method. This resampling method optimizes the distribution of resampled particles by the five operators, i.e., selection, roughening, classification, crossover, and mutation. The proposed resampling method is then integrated into the particle filtering framework to form a genetic optimization resampling based particle filtering (GORPF) algorithm. The performance of the GORPF algorithm is tested by a one-dimensional tracking simulation and a three-dimensional indoor tracking experiment. Both test results show that with the aid of the proposed resampling method, the GORPF has better robustness against particle impoverishment and achieves better positioning accuracy than several existing target tracking algorithms. Moreover, the GORPF algorithm owns an affordable computation load for real-time applications.

A Survey on Moving Target Tracking in the Intelligent Visual Monitoring System

2014 ◽  
Vol 599-601 ◽  
pp. 790-793 ◽  
Author(s):  
Meng Xin Li ◽  
Gao Ling Su ◽  
Jing Hou ◽  
Dai Zheng
Keyword(s):  
Target Tracking ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
Visual Surveillance ◽  
Moving Target ◽  
Extended Kalman Filtering ◽  
Moving Target Tracking ◽  
Filtering Algorithm ◽  
Tracking Algorithms ◽  
Kalman Filtering Algorithm

Moving target tracking is the key part of intelligent visual surveillance system. Among the various tracking algorithms, the Beysian tracking algorithms and the kernel tracking algorithm are two algorithms that frequently used. The Beysian tracking algorithms mainly conclude Kalman filtering algorithm, extended Kalman filtering algorithm and particle filtering algorithm. Mean Shift is the most representative algorithm of the kernel target tracking. In this survey, the status and development of target tracking algorithms has been studied more extensively with providing a few examples of modified tracking algorithms. Then a comparison was presented based on the limitations and scope of applications. Finally, the paper showed further research prospects of moving target tracking are introduced.

Error-Ellipse-Resampling-Based Particle Filtering Algorithm for Target Tracking

2020 ◽  
Vol 20 (10) ◽  
pp. 5389-5397 ◽  
Author(s):  
Xinxin Wang ◽  
Cheng Xu ◽  
Shihong Duan ◽  
Jiawang Wan
Keyword(s):  
Target Tracking ◽  
Particle Filtering ◽  
Error Ellipse ◽  
Filtering Algorithm

Compressed Iterative Particle Filter for Target Tracking

2011 ◽  
Vol 55-57 ◽  
pp. 91-94
Author(s):  
Hong Bo Zhu ◽  
Hai Zhao ◽  
Dan Liu ◽  
Chun He Song
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
Filtering Algorithms ◽  
Filtering Algorithm ◽  
Ga Algorithm ◽  
Non Linear

Particle filtering has been widely used in the non-linear n-Gaussian target tracking problems. The main problem of particle filtering is the lacking and exhausting of particles, and choosing effective proposed distribution is the key point to overcome it. In this paper, a new mixed particle filtering algorithm was proposed. Firstly, the unscented kalman filtering is used to generate the proposed distribution, and in the resample step, a new certain resample method is used to choose the particles with ordered larger weights. GA algorithm is introduced into the certain resample method to keep the variety of the particles. Simuational results have shown that the proposed algorithm has better performances than other three typical filtering algorithms.

Sign in / Sign up

Export Citation Format

Share Document