A decentralised particle filtering algorithm for multi-target tracking across multiple flight vehicles

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

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

Remote Sensing ◽

10.3390/rs13010132 ◽

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.

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A New Multi-sensor Particle CPHD Filtering Algorithm for Bearings-only Multi-target Tracking

Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019) ◽

10.2991/eee-19.2019.24 ◽

2019 ◽

Author(s):

Jun-gen Zhang

Keyword(s):

Target Tracking ◽

Filtering Algorithm ◽

Multi Target Tracking

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.790 ◽

2014 ◽

Vol 599-601 ◽

pp. 790-793 ◽

Cited By ~ 2

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.

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