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An Improved gaussian particle filter Algorithm Using KLD-Sampling

Transactions of Nanjing University of Aeronautics and Astronautics 2020年第4期37卷 607-614页

作者： ZHOU Zhaihe ZHONG Yulu ZENG Qingxi TIAN Xiangrui College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

To adjust the samples of filtering adaptively,an improved gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this *** the process of sampling,the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density *** has significant effect when the noise obeys gaussian distribution and the statistical characteristics of noise change *** results show that KLGPF could maintain a good estimation effect when the noise statistics changes *** with the particle filter algorithm using KLD-sampling(KLPF),the speed of KLGPF increases by 28%under the same conditions.

关键词： particle filter gaussian particle filter KLD-sampling noise mutation adaptive particle numbers

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Correlation gaussian particle filter for Robust Visual Tracking

Correlation Gaussian Particle Filter for Robust Visual Track...

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第32届中国控制与决策会议

作者： Juan Zhang Zhigang Liu Yuehan Lin School of Computer and Communication Engineering Northeastern University

Visual tracking plays a significant role in computer *** filter（CF） based visual tracking algorithms have made great progress owing to their high ***,the traditional CF based tracking algorithm uses the fixed-size template,when the target changes in size,the tracker cannot track the object *** address the issues,A novel accurate position and scale estimation algorithm that combination kernel correlation filter（KCF） with gaussian particle filter（CGPF） for visual tracking has been *** algorithm estimates the position of the target by KCF,and then corrects the position by a gaussian particle filter *** the problem of scale variation information in the KCF,A scale correlation filter is constructed,and it is used to identify the scale with the maximum response to select the target ***,The comprehensive experiments on OTB-50 visual tracking benchmark datasets are *** experiments results show that the proposed approach outperforms the KCF *** the results also prove availability of our method.

关键词： Visual tracking Kernel correlation filter gaussian particle filter Scale variation

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Object Detection and Tracking Method of AUV Based on Acoustic Vision

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China Ocean Engineering 2012年第4期26卷 623-636页

作者：张铁栋万磊曾文静徐玉如 National Key Laboratory of Science and Technology on Autonomous Underwater Vehicle Harbin Engineering University

This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow （underwater acoustic images） is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.

关键词： AUV acoustic image object detection gaussian particle filter object tracking

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