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Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming 被引量:9
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作者 TIAN Chen PEI Yang +1 位作者 HOU Peng ZHAO Qian 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第5期859-870,共12页
Multi-range-false-target(MRFT) jamming is particularly challenging for tracking radar due to the dense clutter and the repeated multiple false targets. The conventional association-based multi-target tracking(MTT) met... Multi-range-false-target(MRFT) jamming is particularly challenging for tracking radar due to the dense clutter and the repeated multiple false targets. The conventional association-based multi-target tracking(MTT) methods suffer from high computational complexity and limited usage in the presence of MRFT jamming.In order to solve the above problems, an efficient and adaptable probability hypothesis density(PHD) filter is proposed. Based on the gating strategy, the obtained measurements are firstly classified into the generalized newborn target and the existing target measurements. The two categories of measurements are independently used in the decomposed form of the PHD filter. Meanwhile,an amplitude feature is used to suppress the dense clutter. In addition, an MRFT jamming suppression algorithm is introduced to the filter. Target amplitude information and phase quantization information are jointly used to deal with MRFT jamming and the clutter by modifying the particle weights of the generalized newborn targets. Simulations demonstrate the proposed algorithm can obtain superior correct discrimination rate of MRFT, and high-accuracy tracking performance with high computational efficiency in the presence of MRFT jamming in the dense clutter. 展开更多
关键词 multi-range-false-target(MRFT)jamming multi-target tracking(MTT) probability hypothesis density(PHD) target amplitude feature gating strategy
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A multi-target tracking algorithm based on Gaussian mixture model 被引量:3
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作者 SUN Lili CAO Yunhe +1 位作者 WU Wenhua LIU Yutao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第3期482-487,共6页
Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is ... Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is proposed.The algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior probability.Compared with the traditional data association algorithm,this algorithm has better tracking performance and less computational complexity.Simulation results demonstrate the effectiveness of the proposed algorithm. 展开更多
关键词 multiple-target tracking Gaussian mixture model(GMM) data association expectation maximization(EM)algorithm
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A novel maneuvering multi-target tracking algorithm based on multiple model particle filter in clutters 被引量:2
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作者 胡振涛 Pan Quan Yang Feng 《High Technology Letters》 EI CAS 2011年第1期19-24,共6页
To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle fi... To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory analysis and simulation results show the validity of the method. 展开更多
关键词 maneuvering multi-target tracking multiple model particle filter interacting multiple model IMM) joint probabilistic data association
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Computer Model for Evaluating Multi-Target Tracking Algorithms
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作者 Garret Vo Chiwoo Park 《Open Journal of Modelling and Simulation》 2019年第1期1-18,共18页
Public benchmark datasets have been widely used to evaluate multi-target tracking algorithms. Ideally, the benchmark datasets should include the video scenes of all scenarios that need to be tested. However, a limited... Public benchmark datasets have been widely used to evaluate multi-target tracking algorithms. Ideally, the benchmark datasets should include the video scenes of all scenarios that need to be tested. However, a limited amount of the currently available benchmark datasets does not comprehensively cover all necessary test scenarios. This limits the evaluation of multitarget tracking algorithms with various test scenarios. This paper introduced a computer simulation model that generates benchmark datasets for evaluating multi-target tracking algorithms with the complexity of multitarget tracking scenarios directly controlled by simulation inputs such as target birth and death rates, target movement, the rates of target merges and splits, target appearances, and image noise types and levels. The simulation model generated a simulated video and also provides the ground-truth target tracking for the simulated video, so the evaluation of multitarget tracking algorithms can be easily performed without any manual video annotation process. We demonstrated the use of the proposed simulation model for evaluating tracking-by-detection algorithms and filtering-based tracking algorithms. 展开更多
关键词 Performance EVALUATION multi-target tracking COMPUTER Model SIMULATION
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Maneuvering Multi-target Tracking Algorithm Based on Modified Generalized Probabilistic Data Association
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作者 Zhentao Hu Chunling Fu Xianxing Liu 《Engineering(科研)》 2011年第12期1155-1160,共6页
Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multitarget tracking algorithm based on modified generalized probabilis... Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multitarget tracking algorithm based on modified generalized probabilistic data association is proposed in this paper. In view of the advantage of particle filter which can deal with the nonlinear and non-Gaussian system, it is introduced into the framework of generalized probabilistic data association to calculate the residual and residual covariance matrices, and the interconnection probability is further optimized. On that basis, the dynamic combination of particle filter and generalized probabilistic data association method is realized in the new algorithm. The theoretical analysis and experimental results show the filtering precision is obviously improved with respect to the tradition method using suboptimal filter. 展开更多
关键词 multi-target tracking PARTICLE FILTER GENERALIZED PROBABILISTIC Data ASSOCIATION Clutters
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Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar
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作者 Xuchong Yi Shuangxi Zhang Yuxuan Zhou 《Geodesy and Geodynamics》 EI CSCD 2024年第5期504-515,共12页
Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal c... Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios. 展开更多
关键词 2D-MUSIC FMCW radar Moving target tracking SUPER-RESOLUTION algorithm optimization
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WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm
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作者 Duo Peng Kun Xie Mingshuo Liu 《Journal of Beijing Institute of Technology》 EI CAS 2024年第1期28-40,共13页
A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filte... A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively. 展开更多
关键词 wireless sensor network(WSN)target tracking snake optimization algorithm extended Kalman filter maneuvering target
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Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming
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作者 Zhengjie Li Junwei Xie +1 位作者 Haowei Zhang Jiahao Xie 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2023年第6期414-427,共14页
The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In t... The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case. 展开更多
关键词 Netted radar system MIMO Target assignment Power allocation multi-targets tracking Self-defense blanket jamming
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Multi-Target Tracking of Person Based on Deep Learning
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作者 Xujun Li Guodong Fang +1 位作者 Liming Rao Tengze Zhang 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期2671-2688,共18页
To improve the tracking accuracy of persons in the surveillance video,we proposed an algorithm for multi-target tracking persons based on deep learning.In this paper,we used You Only Look Once v5(YOLOv5)to obtain pers... To improve the tracking accuracy of persons in the surveillance video,we proposed an algorithm for multi-target tracking persons based on deep learning.In this paper,we used You Only Look Once v5(YOLOv5)to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric(DeepSORT)to do cascade matching and Intersection Over Union(IOU)matching of person targets between different frames.To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification(ReID)network in the process of cascade matching,we introduced Spatial Relation-aware Global Attention(RGA-S)and Channel Relation-aware Global Attention(RGA-C)attention mechanisms into the network structure.The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03.To enhance the discrimination performance of the network,we proposed a new loss function design method,which introduces the Hard-Negative-Mining way into the benchmark triplet loss.To improve the classification accuracy of the network,we introduced a Label-Smoothing regularization method to the cross-entropy loss.To facilitate the model’s convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training,this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay.The experimental results on CUHK03 show that the mean Average Precision(mAP)of the improved ReID network is 76.5%.The Top 1 is 42.5%,the Top 5 is 65.4%,and the Top 10 is 74.3%in Cumulative Matching Characteristics(CMC);Compared with the original algorithm,the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%,the tracking precision is improved by 3.8%.The number of identity switching is reduced by 25%.The algorithm effectively alleviates the IDSwitch problem,improves the tracking accuracy of persons,and has a high practical value. 展开更多
关键词 YOLOv5 DeepSORT deep learning attention mechanism person re-identification multi-target tracking
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Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality
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作者 Tingting Yang Shuwen Jia +1 位作者 Ying Yu Zhiyong Sui 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期1691-1704,共14页
Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation ... Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user’s viewpoint(or that of a camera)with regard to the virtual content’s coordinate sys-tem.Therefore,the real-time establishment of 3-dimension(3D)maps in real scenes is particularly important for augmented reality technology.So in this paper,we integrate Simultaneous Localization and Mapping(SLAM)technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF(ORB)feature extraction and optimized key frame selection,as well as the use of the Progressive Sample Consensus(PROSAC)algorithm for planar estimation of augmented reality implementations,thus solving the problem of increased sys-tem runtime because of the loss of large amounts of texture information in images.In this paper,we get better results by comparing experiments and data analysis.However,there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points. 展开更多
关键词 Markerless tracking algorithm ORB-SLAM2 framework algorithm augmented reality
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Vehicle recognition and tracking based on simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm
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作者 王伟峰 YANG Bo +1 位作者 LIU Hanfei QIN Xuebin 《High Technology Letters》 EI CAS 2023年第2期113-121,共9页
Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific... Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be determined.In this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is proposed.The specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter algorithm.The proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving objects.The experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring range.The root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional methods.The proposed algorithm has very good application value. 展开更多
关键词 vehicle recognition target tracking annealing chaotic particle swarm Gauss particle filter(GPF)algorithm
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FGAs-Based Data Association Algorithm for Multi-sensor Multi-target Tracking 被引量:4
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作者 朱力立 张焕春 经亚枝 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2003年第3期177-181,共5页
A novel data association algorithm is developed based on fuzzy geneticalgorithms (FGAs). The static part of data association uses one FGA to determine both the lists ofcomposite measurements and the solutions of m-bes... A novel data association algorithm is developed based on fuzzy geneticalgorithms (FGAs). The static part of data association uses one FGA to determine both the lists ofcomposite measurements and the solutions of m-best S-D assignment. In the dynamic part of dataassociation, the results of the m-best S-D assignment are then used in turn, with a Kalman filterstate estimator, in a multi-population FGA-based dynamic 2D assignment algorithm to estimate thestates of the moving targets over time. Such an assignment-based data association algorithm isdemonstrated on a simulated passive sensor track formation and maintenance problem. The simulationresults show its feasibility in multi-sensor multi-target tracking. Moreover, algorithm developmentand real-time problems are briefly discussed. 展开更多
关键词 multi-target tracking data association FGA assignment problem kalmanfilter
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An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to Multi-Target Tracking 被引量:3
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作者 Jacob H.White Randal W.Beard 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第4期942-953,共12页
This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images ... This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit(IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking(MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/Hh K-p2 h XNn U. 展开更多
关键词 Aerial robotics epipolar geometry multi-target tracking pose estimation unmanned aircraft systems vision-based flight
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A new algorithm of bearings-only multi-target tracking of bistatic system 被引量:2
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作者 Benlian XU Zhiquan WANG 《控制理论与应用(英文版)》 EI 2006年第4期331-337,共7页
Much research mainly focuses on the batch processing method (e.g. maximum likelihood method) when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive ... Much research mainly focuses on the batch processing method (e.g. maximum likelihood method) when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive processing method is presented and employed, and corresponding data association algorithms, i.e. a multi-objective ant-colony-based optimization algorithm and an easy fast assignment algorithm are developed to solve the measurements-to-measurements and measurements-to-tracks data association problems of bistatic sonar system, respectively. Monte-Carlo simulations are induced to evaluate the effectiveness of the proposed methods. 展开更多
关键词 BEARINGS-ONLY multi-target tracking Data association Ant colony optimization
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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model 被引量:1
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作者 Tamilvizhi Thanarajan Youseef Alotaibi +1 位作者 Surendran Rajendran Krishnaraj Nagappan 《Computers, Materials & Continua》 SCIE EI 2023年第8期1995-2013,共19页
Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communi... Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches. 展开更多
关键词 Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis
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Application of Predictive Algorithm in Head-Tracking System
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作者 姜明 李科杰 李鑫 《Journal of Beijing Institute of Technology》 EI CAS 2002年第3期306-310,共5页
The worldwide research status of head tracking is introduced and the works made in the research of the predictive algorithm and in the exploration of the rule of the head tracking are set forth. A time delay model f... The worldwide research status of head tracking is introduced and the works made in the research of the predictive algorithm and in the exploration of the rule of the head tracking are set forth. A time delay model for the telerobotic scout system is built. In respect of eliminating error caused by time delay and making reasonable prediction to the data stream, many methods are experimented in order to realize the aim of real time tracking. The application of extrapolation algorithm and auto recursive algorithm in the orientation tracking is described in detail. These two algorithms are realized in Matlab environment. Through analysis of the curves generated by using these two predictive algorithms, an appropriate method was applied in the telerobotic scout system. The effect is satisfying. 展开更多
关键词 TELEOPERATION head tracking predictive algorithm
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Multiple Object Tracking through Background Learning 被引量:1
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作者 Deependra Sharma Zainul Abdin Jaffery 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期191-204,共14页
This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,th... This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,that involves a frame of reference in spatial domain to localize and/or track any object.Thefield of multiple object tracking has seen a lot of research,but researchers have considered the background as redundant.However,in object tracking,the back-ground plays a vital role and leads to definite improvement in the overall process of tracking.In the present work an algorithm is proposed for the multiple object tracking through background learning.The learning framework is based on graph embedding approach for localizing multiple objects.The graph utilizes the inher-ent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple objects.The proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation measures.It is observed that our proposed algorithm gives better performance. 展开更多
关键词 Object tracking image processing background learning graph embedding algorithm computer vision
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Target Tracking Algorithm Based on Meanshift and Kalman Filter 被引量:4
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作者 Hua Li Jia Zhu 《Journal of Beijing Institute of Technology》 EI CAS 2019年第2期365-370,共6页
Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalma... Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalman filtering algorithm to determine the position of the target centroid and subsequently adjust the current search window adaptively according to the target centroid position and the previous frame search window boundary.The derived search window is more closely matched to the location of the target,which improves the accuracy and reliability of tracking.The environmental influence and other influencing factors on the algorithm are also reduced.Through comparison and analysis of the experiments,the modified algorithm demonstrates good stability and adaptability,and can effectively solve the problem of large area occlusion and similar interference. 展开更多
关键词 TARGET tracking MEANSHIFT algorithm KALMAN algorithm
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Dynamic cluster member selection method for multi-target tracking in wireless sensor network 被引量:8
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作者 蔡自兴 文莎 刘丽珏 《Journal of Central South University》 SCIE EI CAS 2014年第2期636-645,共10页
Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a s... Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection. 展开更多
关键词 wireless sensor networks multi-target tracking collaborative task allocation dynamic cluster comprehensive performance index function
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Research on Trajectory Tracking Method of Redundant Manipulator Based on PSO Algorithm Optimization 被引量:2
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作者 Shifu Xu Yanan Jiang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第10期401-415,共15页
Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PS... Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PSO algorithm optimization is studied.The kinematic diagram of redundant manipulator is created,to derive the equation of motion trajectory of redundant manipulator end.Pseudo inverse Jacobi matrix is used to solve the problem of manipulator redundancy.Based on the tracking ellipse of redundant manipulator,the tracking shape of redundant manipulator is determined with the overall tracking index as the second index,and the optimization method of tracking index is proposed.The redundant manipulator contour is located by active contour model,on this basis,combined with particle swarm optimization algorithm,the point coordinates on the circumference with the relevant joint point as the center and joint length as the radius are selected as the algorithm particles for iteration,and the optimal tracking results of the overall redundant manipulator trajectory are obtained.The experimental results show that under the proposed method,the tracking error of the redundant manipulator is low,and the error jump range is small.It shows that this method has high tracking accuracy and reliability. 展开更多
关键词 PSO algorithm optimization redundant manipulator TRAJECTORY tracking overall tracking index
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