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FGAs-Based Data Association Algorithm for Multi-sensor Multi-target Tracking 认领 引用 被引量：7
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作者 朱力立 张焕春 经亚枝 《Chinese Journal of Aeronautics》 EI CAS 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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Resource management strategy for maneuvering multi-target tracking in cognitive radar network under non-ideal communication 认领 引用
2
作者 Congchong Cao Ting Cheng +1 位作者 Zishu He Quan Zhou 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第7期280-298,共19页
A joint node selection and power allocation(JNSPA)resource management strategy is proposed in this study to minimize the total system power consumption in cognitive radar network(CRN)under non-ideal communication,whil... A joint node selection and power allocation(JNSPA)resource management strategy is proposed in this study to minimize the total system power consumption in cognitive radar network(CRN)under non-ideal communication,while maintaining desired tracking accuracy.Firstly,the predicted conditional Cramér-Rao Lower Bound(PC-CRLB)under non-ideal communication is derived for evaluating tracking performance,where the impact of data loss between local radars(LRs)and fusion center(FC)is comprehensively considered.In the derivation of the PC-CRLB,the data information from position measurement and pseudo-measurement are taken into consideration sequentially to match the following adaptive linear sequential filtering algorithm.Furthermore,an optimization model is formulated to minimize the total system power consumption,subject to system constraints.Due to the coupling of optimization variables and the inclusion of binary variables,the problem is a mixed-integer nonlinear optimization problem with NP-hard characteristics.To solve this,an alternating optimization method based on task priority and node contribution(TPNC)algorithm is proposed for efficient solu-tion.Finally,simulation results demonstrate that the resource management strategy generated by FC can effectively guide the LRs,validating the effectiveness,superiority,and real-time applicability of proposed JNSPA strategy. 展开更多
关键词 Resource management Radar networks Non-ideal communication Maneuvering multi-target tracking
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Joint multi-dimensional resource scheduling for cooperative tracking of multiple LEO targets via space-based radar networks 认领 引用
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作者 Qingwei YANG Libing JIANG +2 位作者 Shuyu ZHENG Yingjian ZHAO Zhuang WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期449-466,共18页
Appropriate resource scheduling is the key to achieving the best performance of Low Earth-Orbit(LEO)Multi-Target Tracking(MTT)for the Space-based Radar Networks(SBRN)system.The high-velocity nature of satellites impos... Appropriate resource scheduling is the key to achieving the best performance of Low Earth-Orbit(LEO)Multi-Target Tracking(MTT)for the Space-based Radar Networks(SBRN)system.The high-velocity nature of satellites imposes high demands on the convergence rate of filtering and tracking accuracy while escalating the complexity of dynamic scheduling.To address these challenges,this paper utilizes Two-Line Elements(TLE)information to fully exploit the limited power-aperture resource of space-based Colocated Multiple-Input Multiple-Output(C-MIMO)radar and constructs a Walker constellation SBRN system.On this basis,a cognitive distributed cooperative tracking framework with fusion feedback mechanism is proposed.Each node can serve as the fusion center,achieving optimal fusion through the Fast Covariance Intersection(FCI)criterion while reducing the communication requirements.The global outcomes are fed back to all local nodes which can hasten the convergence rate of target state estimation.To maximize the global MTT performance,a closed-loop Joint Multi-Dimensional Resource Scheduling(JMDRS)strategy that considers multi-coverage conditions and visible windows is established.Moreover,the Posterior Cramer-Rao Lower Bound under Global Fusion Feedback(GF-PCRLB)is derived to provide a quantifiable metric for the overall performance.Finally,a fast suboptimal solution to the nonconvex model is proposed based on cross-iterative dimension reduction and variable decoupling.It introduces the tracking Efficiency-to-Cost Ratio(ECR)to jointly decide the beam pointing as well as the transmit power and fusion weight of each beam.Numerical results demonstrate that the proposed method significantly outperforms the existing approaches in enhancing the MTT performance of the SBRN system. 展开更多
关键词 Dynamic resource allocation Moving platform cooperative tracking Multi-Target Tracking(MTT) Space-based Radar Networks(SBRN) Task management
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Extended GMPHD with amplitude information for multi-sensor multi-target tracking 认领 引用 被引量：1
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作者 Weizhen Ma Zhongliang Jing Peng Dong 《Aerospace Systems》 2021年第4期271-279,共9页
To make a better discrimination between target and false alarm,amplitude information(AI)of radar signal has been incorporated into many target tracking algorithms,such as probabilistic data association,multiple hypoth... To make a better discrimination between target and false alarm,amplitude information(AI)of radar signal has been incorporated into many target tracking algorithms,such as probabilistic data association,multiple hypothesis tracking and probability density hypothesis(PHD).In this paper,we present the cubature integration based Gaussian mixture implementation of the PHD filterwith AI,namely the GMPHD-AI filter.Since the utilization ofmultiple sensors ismore effective than using only one sensor,we also extend the GMPHD-AI filter to the multi-sensor application where the iterated-corrector scheme is exploited for multi-sensor information fusion.The effectiveness of the presented method is demonstrated by a multiple targets tracking scenario. 展开更多
关键词 Random Finite Set Multi-target tracking Multi-sensor fusion PHD filter Amplitude information Iterated-corrector
Dynamic cluster member selection method for multi-target tracking in wireless sensor network 认领 引用 被引量：10
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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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Online multi-target intelligent tracking using a deep long-short term memory network 认领 引用 被引量：3
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作者 Yongquan ZHANG Zhenyun SHI +1 位作者 Hongbing JI Zhenzhen SU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2023年第9期313-329,共17页
Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In ... Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios. 展开更多
关键词 Data association Deep long-short term memory network Historical sequence Multi-target tracking Target tuple set Track management
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METHOD OF MULTI-TARGET TRACKING IN WIDE AREA SURVEILLANCE AIRBORNE RADAR SYSTEM BASING ON CLUSTERING ANALYSIS 认领 引用 被引量：3
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作者 Wu Kun Zhao Fengjun +2 位作者 Hui Zhou Zheng Shichao Zheng Mingjie 《Journal of Electronics(China)》 2014年第3期208-213,共6页
This paper proposed a robust method based on the definition of Mahalanobis distance to track ground moving target. The feature and the geometry of airborne ground moving target tracking systems are studied at first. B... This paper proposed a robust method based on the definition of Mahalanobis distance to track ground moving target. The feature and the geometry of airborne ground moving target tracking systems are studied at first. Based on this feature, the assignment relation of time-nearby target is calculated via Mahalanobis distance, and then the corresponding transformation formula is deduced. The simulation results show the correctness and effectiveness of the proposed method. 展开更多
关键词 Airborne radar Wide Area Surveillance(WAS) Moving target detect Multi-Target Tracking(MTT)
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Performance evaluation for multi-target tracking with temporal dimension specifics 认领 引用 被引量：2
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作者 Zhenzhen SU Hongbing JI +1 位作者 Cong TIAN Yongquan ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第2期446-458,共13页
With the great development of Multi-Target Tracking(MTT)technologies,many MTT algorithms have been proposed with their own advantages and disadvantages.Due to the fact that requirements to MTT algorithms vary from the... With the great development of Multi-Target Tracking(MTT)technologies,many MTT algorithms have been proposed with their own advantages and disadvantages.Due to the fact that requirements to MTT algorithms vary from the application scenarios,performance evaluation is significant to select an appropriate MTT algorithm for the specific application scenario.In this paper,we propose a performance evaluation method on the sets of trajectories with temporal dimension specifics to compare the estimated trajectories with the true trajectories.The proposed method evaluates the estimate results of an MTT algorithm in terms of tracking accuracy,continuity and clarity.Furthermore,its computation is based on a multi-dimensional assignment problem,which is formulated as a computable form using linear programming.To enhance the influence of recent estimated states of the trajectories in the evaluation,an attention function is used to reweight the trajectory errors at different time steps.Finally,simulation results show that the proposed performance evaluation method is able to evaluate many aspects of the MTT algorithms.These evaluations are worthy for selecting suitable MTT algorithms in different application scenarios. 展开更多
关键词 Multi-target tracking Temporal dimension specifics Performance evaluation Random finite sets Linear programming
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Adaptive resource management for multi-target tracking in co-located MIMO radar based on time-space joint allocation 认领 引用 被引量：2
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作者 SU Yang CHENG Ting +2 位作者 HE Zishu LI Xi LU Yanxi 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2020年第5期916-927,共12页
Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom deg... Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking,this paper proposes a resource management optimization model,where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar. 展开更多
关键词 co-located multiple-input multiple-output(MIMO)radar adaptive resource management multi-target tracking sub-array division time-space joint allocation
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Multi-Target Tracking of Person Based on Deep Learning 认领 引用 被引量：1
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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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A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking 认领 引用 被引量：3
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作者 胡振涛 Fu Chunling 《High Technology Letters》 EI CAS 2014年第4期355-362,共8页
Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.C... Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm. 展开更多
关键词 multi-sensor information fusion weight optimization correlated noises maneuvering target tracking
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A novel maneuvering multi-target tracking algorithm based on multiple model particle filter in clutters 认领 引用 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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Kernel density estimation and marginalized-particle based probability hypothesis density filter for multi-target tracking 认领 引用 被引量：3
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作者 张路平 王鲁平 +1 位作者 李飚 赵明 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第3期956-965,共10页
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
关键词 particle filter with probability hypothesis density marginalized particle filter meanshift kernel density estimation multi-target tracking
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MULTI-TARGET VISUAL TRACKING AND OCCLUSION DETECTION BY COMBINING BHATTACHARYYA COEFFICIENT AND KALMAN FILTER INNOVATION 认领 引用 被引量：1
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作者 Chen Ken Chul Gyu Jhun 《Journal of Electronics(China)》 2013年第3期275-282,共8页
This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author's previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this p... This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author's previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this paper a method of combining Bhattacharyya Coefficient (BC) and Kalman filter innovation term is proposed as the criteria for jointly detecting the occlusion occurrence. Fragmentation of target is introduced in order to closely monitor the occlusion development. In the course of occlusion, the Kalman predictor is applied to determine the location of the occluded target, and the criterion for checking the re-appearance of the occluded target is also presented. The proposed approach is put to test on a standard video sequence, suggesting the satisfactory performance in multi-target tracking. 展开更多
关键词 Visual tracking Multi-target occlusion Bhattacharyya Coefficient (BC) Kalman filter
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Robust attitude control for rapid multi-target tracking in spacecraft formation flying 认领 引用
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作者 袁长清 李俊峰 +1 位作者 王天舒 宝音贺西 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2008年第2期185-198,共14页
A robust attitude tracking control scheme for spacecraft formation flying is presented. The leader spacecraft with a rapid mobile antenna and a camera is modeled. While the camera is tracking the ground target, the an... A robust attitude tracking control scheme for spacecraft formation flying is presented. The leader spacecraft with a rapid mobile antenna and a camera is modeled. While the camera is tracking the ground target, the antenna is tracking the follower spacecraft. By an angular velocity constraint and an angular constraint, two methods are proposed to compute the reference attitude profiles of the camera and antenna, respectively. To simplify the control design problem, this paper first derives the desired inverse system （DIS）, which can convert the attitude tracking problem of 3D space into the regulator problem. Based on DIS and sliding mode control （SMC）, a robust attitude tracking controller is developed in the presence of mass parameter uncertainties and external disturbance. By Lyapunov stability theory, the closed loop system stability can be achieved. The numerical simulations show that the proposed robust control scheme exhibits significant advantages for the multi-target attitude tracking of a two-spacecraft formation. 展开更多
关键词 attitude control formation flying multi-body spacecraft robust control,multi-target tracking
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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 认领 引用 被引量：7
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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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Cardinality compensation method based on information-weighted consensus filter using data clustering for multi-target tracking 认领 引用 被引量：4
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作者 Sunyoung KIM Changho KANG Changook PARK 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2019年第9期2164-2173,共10页
In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hy... In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques. 展开更多
关键词 Cardinality compensation Cardinalized probability hypothesis density filter Data clustering Information-weighted consensus filter Multi-target tracking
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Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming 认领 引用 被引量：7
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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 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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An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to Multi-Target Tracking 认领 引用 被引量：6
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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 http://gffzz6712f30e645c4b03skfqq69bqk6b06qu5.ffgz.tsg.suse.edu.cn/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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Graph neural network‑tracker:a graph neural network‑based multi‑sensor fusion framework for robust unmanned aerial vehicle tracking 认领 引用
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作者 Karim Dabbabi Tijeni Delleji 《Visual Computing for Industry,Biomedicine,and Art》 EI 2025年第1期277-287,共11页
Unmanned aerial vehicle(UAV)tracking is a critical task in surveillance,security,and autonomous navigation applications.In this study,we propose graph neural network-tracker(GNN-tracker),a novel GNN-based UAV tracking... Unmanned aerial vehicle(UAV)tracking is a critical task in surveillance,security,and autonomous navigation applications.In this study,we propose graph neural network-tracker(GNN-tracker),a novel GNN-based UAV tracking framework that effectively integrates graph-based spatial-temporal modelling,Transformer-based feature extraction,and multi-sensor fusion to enhance tracking robustness and accuracy.Unlike traditional tracking approaches,GNNtracker dynamically constructs a spatiotemporal graph representation,improving identity consistency and reducing tracking errors under OCC-heavy scenarios.Experimental evaluations on optical,thermal,and fused UAV datasets demonstrate the superiority of GNN-tracker(fused)over state-of-the-art methods.The proposed model achieves multiple object tracking accuracy(MOTA)scores of 91.4%(fused),89.1%(optical),and 86.3%(thermal),surpassing TransT by 8.9%in MOTA and 7.7%in higher order tracking accuracy(HOTA).The HOTA scores of 82.3%(fused),80.1%(optical),and 78.7%(thermal)validate its strong object association capabilities,while its frames per second of 58.9(fused),56.8(optical),and 54.3(thermal)ensures real-time performance.Additionally,ablation studies confirm the essential role of graph-based modelling and multi-sensor fusion,with performance drops of up to 8.9%in MOTA when these components are removed.Thus,GNN-tracker(fused)offers a highly accurate,robust,and efficient UAV tracking solution,effectively addressing real-world challenges across diverse environmental conditions and multiple sensor modalities. 展开更多
关键词 Unmanned aerial vehicle tracking Graph neural network Multi-sensor fusion Transformer network Realtime tracking Spatiotemporal modelling Deep learning Optical-thermal fusion
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