针对夜间低光照场景下目标特征提取困难和跟踪不稳定的问题,提出了基于自编码器结构及改进Bytetrack的多目标行人检测及跟踪算法。在检测阶段,基于YOLOX(you only look once X)搭建多任务自编码变换模型框架,以一种自监督的方式考虑物...针对夜间低光照场景下目标特征提取困难和跟踪不稳定的问题,提出了基于自编码器结构及改进Bytetrack的多目标行人检测及跟踪算法。在检测阶段,基于YOLOX(you only look once X)搭建多任务自编码变换模型框架,以一种自监督的方式考虑物理噪声模型和图像信号处理(image signal processing,ISP)的过程,通过对真实光照退化变换过程进行编码与解码学习内在视觉结构,并基于这种表示通过解码边界框坐标与类实现目标检测任务。为了抑制背景噪声的干扰,在目标解码器颈部网络引入自适应特征融合模块ASFF。跟踪阶段,基于Bytetrack算法进行改进,将基于Tranformer重识别网络提取到的外观嵌入信息与NSA卡尔曼滤波获得的运动信息通过自适应加权的方法完成数据关联,并通过Byte两次匹配的算法完成夜间行人的跟踪。在自建夜间行人检测数据集上测试检测模型的泛化能力,mAP@0.5达到了94.9%,结果表明本文的退化变换过程符合现实条件,具有良好的泛化能力。最后通过自建夜间行人跟踪数据集验证多目标跟踪性能,实验结果表明,本文提出的夜间低光照行人多目标跟踪算法MOTA(multiple object tracking accuracy)为89.55%,IDF1(identity F1 score)为88.34%,IDs(ID switches)为15。与基准方法Bytetrack相比,MOTA提高了10.72%,IDF1提高了6.19%,IDs减少了50%。结果表明,本文提出的基于自编码结构及改进Bytetrack的多目标跟踪算法可以有效解决在夜间低光照场景下行人跟踪困难的问题。展开更多
A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form ...A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form different lengths of robot arms for different application sites.The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space.This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator.In this integrated optimization,path planning is established on a Bezier curve,and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator.A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy.The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve.Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints.The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.展开更多
A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation (MTREC) was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of r...A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation (MTREC) was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation (TREC) technique with a large "box" to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small "box" to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies: the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.展开更多
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
Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are...Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.展开更多
In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ...In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.展开更多
Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method ...Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.展开更多
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
The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. Typically, refl...The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. Typically, reflectivity data from multiple radars are objectively analyzed and mosaiced onto a regional 3-D Cartesian grid prior to being assimilated into the models. One multi-radar observations is the synchronization of all of the scientific issues associated with the mosaic of the observations. Since radar data is usually rapidly updated (-every 5-10 min), it is common in current multi-radar mosaic techniques to combine multiple radar' observations within a time window by assunfing that the storms are steady within the window. The assumption holds well for slow evolving precipitation systems, but for fast evolving convective storms, this assumption may be violated and the mosaic of radar observations at different times may result in inaccurate storm structure depictions. This study investigates the impact of synchronization on storm structures in multiple radar data analyses using a multi-scale storm tracking algorithm.展开更多
A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically...A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.展开更多
<div style="text-align:justify;"> In recent years, multi-target tracking technology based on Gaussian Mixture- Probability Hypothesis Density (GM-PHD) filtering has become a hot field of information fu...<div style="text-align:justify;"> In recent years, multi-target tracking technology based on Gaussian Mixture- Probability Hypothesis Density (GM-PHD) filtering has become a hot field of information fusion research. This article outlines the generation and development of multi-target tracking methods based on GM-PHD filtering, and the principle and implementation method of GM-PHD filtering are explained, and the application status based on GM-PHD filtering is summarized, and the key issues of the development of GM-PHD filtering technology are analyzed. </div>展开更多
This paper presents augmented input estimation(AIE)for multiple maneuvering target tracking.Multi-target tracking(MTT)is based on two main parts,data association and estimation.In data association(DA),the best observa...This paper presents augmented input estimation(AIE)for multiple maneuvering target tracking.Multi-target tracking(MTT)is based on two main parts,data association and estimation.In data association(DA),the best observations are assigned to the considered tracks.In real conditions,the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done simply.In this case,for general MTT problems with unknown numbers of targets,we present a Markov chain Monte-Carlo DA(MCMCDA)algorithm that approximates the optimal Bayesian filter with low complexity in computations.After DA,estimation and tracking should be done.Since in general cases,many targets can have maneuvering motions,then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is eliminated.This model with an input estimation(IE)approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector.Some comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.展开更多
We investigate the tracking control for a class of nonlinear heterogeneous leader-follower multi-agent systems(MAS)with unknown external disturbances. Firstly, the neighbor-based distributed finite-time observers ar...We investigate the tracking control for a class of nonlinear heterogeneous leader-follower multi-agent systems(MAS)with unknown external disturbances. Firstly, the neighbor-based distributed finite-time observers are proposed for the followers to estimate the position and velocity of the leader. Then, two novel distributed adaptive control laws are designed by means of linear sliding mode(LSM) as well as nonsingular terminal sliding mode(NTSM), respectively. One can prove that the tracking consensus can be achieved asymptotically under LSM and the tracking error can converge to a quite small neighborhood of the origin in finite time by NTSM in spite of uncertainties and disturbances. Finally, a simulation example is given to verify the effectiveness of the obtained theoretical results.展开更多
文摘针对夜间低光照场景下目标特征提取困难和跟踪不稳定的问题,提出了基于自编码器结构及改进Bytetrack的多目标行人检测及跟踪算法。在检测阶段,基于YOLOX(you only look once X)搭建多任务自编码变换模型框架,以一种自监督的方式考虑物理噪声模型和图像信号处理(image signal processing,ISP)的过程,通过对真实光照退化变换过程进行编码与解码学习内在视觉结构,并基于这种表示通过解码边界框坐标与类实现目标检测任务。为了抑制背景噪声的干扰,在目标解码器颈部网络引入自适应特征融合模块ASFF。跟踪阶段,基于Bytetrack算法进行改进,将基于Tranformer重识别网络提取到的外观嵌入信息与NSA卡尔曼滤波获得的运动信息通过自适应加权的方法完成数据关联,并通过Byte两次匹配的算法完成夜间行人的跟踪。在自建夜间行人检测数据集上测试检测模型的泛化能力,mAP@0.5达到了94.9%,结果表明本文的退化变换过程符合现实条件,具有良好的泛化能力。最后通过自建夜间行人跟踪数据集验证多目标跟踪性能,实验结果表明,本文提出的夜间低光照行人多目标跟踪算法MOTA(multiple object tracking accuracy)为89.55%,IDF1(identity F1 score)为88.34%,IDs(ID switches)为15。与基准方法Bytetrack相比,MOTA提高了10.72%,IDF1提高了6.19%,IDs减少了50%。结果表明,本文提出的基于自编码结构及改进Bytetrack的多目标跟踪算法可以有效解决在夜间低光照场景下行人跟踪困难的问题。
基金Supported by National Natural Science Foundation of China(Grant No.61733017)Foundation of State Key Laboratory of Robotics of China(Grant No.2018O13)Shanghai Pujiang Program of China(Grant No.18PJD018).
文摘A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form different lengths of robot arms for different application sites.The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space.This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator.In this integrated optimization,path planning is established on a Bezier curve,and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator.A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy.The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve.Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints.The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.
基金This study was supported by the Special Fund for Basic Research and Operation of Chinese Academy of Meteorological Science:Development on quantitative precipitation forecasts for 0-6 h lead times by blending radar-based extrapolation and GRAPES-meso,Observation and retrieval methods of micro-physics,the National Natural Science Foundation of China
文摘A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation (MTREC) was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation (TREC) technique with a large "box" to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small "box" to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies: the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.
基金Projects(90820302,60805027)supported by the National Natural Science Foundation of ChinaProject(200805330005)supported by the Research Fund for the Doctoral Program of Higher Education,ChinaProject(2009FJ4030)supported by Academician Foundation of Hunan Province,China
文摘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.
基金Defense Advanced Research Project "the Techniques of Information Integrated Processing and Fusion" in the Eleventh Five-Year Plan (513060302).
文摘Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.
基金Project(61101185) supported by the National Natural Science Foundation of ChinaProject(2011AA1221) supported by the National High Technology Research and Development Program of China
文摘In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.
基金This paper was supported by the Natural Science Foundation of Jiangsu Province, China (No. BK2004132).
文摘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.
基金Major funding for this research was provided under the United States Federal Aviation Administration (FAA) Aviation Weather Research Program Advanced Weather Radar Technologies Prod-uct Development Team Memorandum Of Understanding(MOU)partial funding was provided under NOAA-University of Oklahoma Cooperative Agreement Grant No. NA17RJ1227, U.S. Department of Commerce
文摘The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. Typically, reflectivity data from multiple radars are objectively analyzed and mosaiced onto a regional 3-D Cartesian grid prior to being assimilated into the models. One multi-radar observations is the synchronization of all of the scientific issues associated with the mosaic of the observations. Since radar data is usually rapidly updated (-every 5-10 min), it is common in current multi-radar mosaic techniques to combine multiple radar' observations within a time window by assunfing that the storms are steady within the window. The assumption holds well for slow evolving precipitation systems, but for fast evolving convective storms, this assumption may be violated and the mosaic of radar observations at different times may result in inaccurate storm structure depictions. This study investigates the impact of synchronization on storm structures in multiple radar data analyses using a multi-scale storm tracking algorithm.
基金Supported by the National Natural Science Foundation of China(6160303040,61433003)Yunnan Applied Basic Research Project of China(201701CF00037)Yunnan Provincial Science and Technology Department Key Research Program(Engineering)(2018BA070)
文摘A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.
基金Supported by the National Natural Science Foundation of China (60634030), the National Natural Science Foundation of China (60702066, 6097219) and the Natural Science Foundation of Henan Province (092300410158).
文摘<div style="text-align:justify;"> In recent years, multi-target tracking technology based on Gaussian Mixture- Probability Hypothesis Density (GM-PHD) filtering has become a hot field of information fusion research. This article outlines the generation and development of multi-target tracking methods based on GM-PHD filtering, and the principle and implementation method of GM-PHD filtering are explained, and the application status based on GM-PHD filtering is summarized, and the key issues of the development of GM-PHD filtering technology are analyzed. </div>
文摘This paper presents augmented input estimation(AIE)for multiple maneuvering target tracking.Multi-target tracking(MTT)is based on two main parts,data association and estimation.In data association(DA),the best observations are assigned to the considered tracks.In real conditions,the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done simply.In this case,for general MTT problems with unknown numbers of targets,we present a Markov chain Monte-Carlo DA(MCMCDA)algorithm that approximates the optimal Bayesian filter with low complexity in computations.After DA,estimation and tracking should be done.Since in general cases,many targets can have maneuvering motions,then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is eliminated.This model with an input estimation(IE)approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector.Some comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.
基金Project supported by the National Natural Science Foundation of China(Grant No.61203142)the Natural Science Foundation of Hebei Province,China(Grant Nos.F2014202206 and F2017202009)
文摘We investigate the tracking control for a class of nonlinear heterogeneous leader-follower multi-agent systems(MAS)with unknown external disturbances. Firstly, the neighbor-based distributed finite-time observers are proposed for the followers to estimate the position and velocity of the leader. Then, two novel distributed adaptive control laws are designed by means of linear sliding mode(LSM) as well as nonsingular terminal sliding mode(NTSM), respectively. One can prove that the tracking consensus can be achieved asymptotically under LSM and the tracking error can converge to a quite small neighborhood of the origin in finite time by NTSM in spite of uncertainties and disturbances. Finally, a simulation example is given to verify the effectiveness of the obtained theoretical results.