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A multi-sensor-system cooperative scheduling method for ground area detection and Target tracking

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Frontiers of Information Technology & Electronic Engineering 2023年第2期24卷 245-258页

作者： Yunpu ZHANG Qiang FU Ganlin SHAN Department of Electronic and Optical Engineering Shijiazhuang CampusArmy Engineering UniversityShijiazhuang050003China

A multi-sensor-system cooperative scheduling method for multi-task collaboration is proposed in this paper.We studied the method for application in ground area detection and Target tracking.The aim of sensor scheduling is to select the optimal sensors to complete the assigned combat tasks and obtain the best combat benefits.First,an area detection model was built,and the method of calculating the detection risk was proposed to quantify the detection benefits in scheduling.Then,combining the information on road constraints and the Doppler blind zone,a ground Target tracking model was established,in which the posterior Carmér-Rao lower bound was applied to evaluate future tracking accuracy.Finally,an objective function was developed which considers the requirements of detection,tracking,and energy consumption control.By solving the objective function,the optimal sensor-scheduling scheme can be obtained.Simulation results showed that the proposed sensor-scheduling method can select suitable sensors to complete the required combat tasks,and provide good performance in terms of area detection,Target tracking,and energy consumption control.

关键词： Sensor scheduling Area detection Target tracking Road constraints Doppler blind zone

Hybrid-driven Gaussian process online learning for highly maneuvering multi-Target tracking

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Frontiers of Information Technology & Electronic Engineering 2023年第11期24卷 1647-1656页

作者： Qiang GUO Long TENG Tianxiang YIN Yunfei GUO Xinliang WU Wenming SONG College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China China National Aeronautical Radio Electronics Research Institute Shanghai 200233China College of Automation Hangzhou Dianzi UniversityHangzhou 310018China

The performance of existing maneuvering Target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory.This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets,leveraging the advantages of both data-driven and model-based algorithms.The time-varying constant velocity model is integrated into the Gaussian process(GP)of online learning to improve the performance of GP prediction.This integration is further combined with a generalized probabilistic data association algorithm to realize multi-Target tracking.Through the simulations,it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.

关键词： Target tracking Gaussian process Data-driven Online learning Model-driven Probabilistic data association

High-accuracy Target tracking for multistatic passive radar based on a deep feedforward neural network

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信息与电子工程前沿:英文版 2023年第8期24卷 1214-1230页

作者： Baoxiong XU Jianxin YI Feng CHENG Ziping GONG Xianrong WAN Electronic Information School Wuhan UniversityWuhan430072China

In radar systems,Target tracking errors are mainly from motion models and nonlinear measurements.When we evaluate a tracking algorithm,its tracking accuracy is the main criterion.To improve the tracking accuracy,in this paper we formulate the tracking problem into a regression model from measurements to target states.A tracking algorithm based on a modified deep feedforward neural network(MDFNN)is then proposed.In MDFNN,a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence,and the optimal measurement sequence size is analyzed.Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter(EKF),unscented Kalman filter(UKF),and recurrent neural network(RNN)based tracking methods under the considered scenarios.

关键词： Deep feedforward neural network Filter layer Passive radar Target tracking tracking accuracy

A robust Poisson multi-Bernoulli filter for multi-Target tracking based on arithmetic average fusion

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Chinese Journal of Aeronautics 2023年第2期36卷 179-190页

作者： Zhenzhen SU Hongbing JI Cong TIAN Yongquan ZHANG School of Computer Science and Technology Xidian UniversityXi'an 710071China School of Electronic Engineering Xidian UniversityXi'an 710071China

The coalescence and missed detection are two key challenges in Multi-Target tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally difficult to handle the above problems simultaneously,such as the Track-Oriented marginal Multi-Bernoulli/Poisson(TOMB/P)and Measurement-Oriented marginal Multi-Bernoulli/Poisson(MOMB/P)filters.Based on the Arithmetic Average(AA)fusion rule,this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli(PMB)filter,which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with coalescence.In order to fuse the different PMB distributions,the Bernoulli components in different Multi-Bernoulli(MB)distributions are associated with each other by Kullback-Leibler Divergence(KLD)minimization.Moreover,an adaptive AA fusion rule is designed on the basis of the exponential fusion weights,which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in MTT.Finally,by comparing with the TOMB/P and MOMB/P filters,the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.

关键词： Arithmetic average fusion Kullback-Leibler divergence Poisson multi-Bernoulli filter Random finite set Target tracking

Debiased conversion measurements based Target tracking with direction cosine measurements

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Journal of Systems Engineering and Electronics 2022年第6期33卷 1140-1150页

作者： LI Lifu CHENG Ting School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Phased array radar’s measurements include two direction cosine and range measurements,which can be obtained in the direction cosine coordinates.State equation of the target is nonlinear with the measurements and in order to solve the nonlinear problem,debiased conversion measurements based Target tracking with direction cosine and range measurements in direction cosine coordinates named DCMKFPreDcos is proposed first in this paper,where the predicted information is introduced to calculate the converted measurement errors’statistical characteristics to eliminate the correlation between measurement noise and the converted measurement errors covariance.When range rate information can be obtained further,based on the above DCMKF-PreDcos’filtering result,the sequential filtering is adopted to process the additional range rate measurement and the DCMKF-PreDcos algorithm with extra range rate information is given.The predicted information is also introduced to calculate the involved statistical characteristics of converted measurements.The effectiveness of the proposed algorithms is shown in simulation results.

关键词： nonlinear filtering range rate information Target tracking direction cosine coordinates predicted information

Target tracking algorithm based on a broad learning system

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Science China(Information Sciences) 2022年第5期65卷 261-263页

作者： Dan ZHANG Tieshan LI C.L.Philip CHEN He YANG Navigation College Dalian Maritime University Innovation and Entrepreneurship Education College Dalian Minzu University School of Automation Engineering University of Electronic Science and Technology of China Computer Science and Engineering College South China University of Technology

Target tracking is one of the most important research topics in the field of computer vision. It has been widely applied in aerospace, automatic monitoring, navigation, human-computer interaction, and artificial intel... 详细信息

关键词： Target tracking Broad learning system Selective search algorithm Multi-cue tracking tracking adaptability

Snake-Hot-Eye-Assisted Multi-Process-Fusion Target tracking Based on a Roll-Pitch Semi-strapdown Infrared Imaging Seeker

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Journal of Bionic Engineering 2022年第4期19卷 1124-1139页

作者： Jian Chen Zichao Zhang Ke Yi Yu Han Zhang Ren College of Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518000China Systems Engineering Research Institute China State Shipbuilding Corporation LimitedBeijing 100094China College of Water Resources and Civil Engineering China Agricultural UniversityBeijing 100083China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Aiming at intercepting large maneuvering targets precisely,the guidance law of advanced self-seeking missiles requires not only inertial line-of-sight(LOS)angular rate but also target maneuvering acceleration.Moreover,the semi-strapdown stabilization platform has lost the ability to measure the inertial LOS angular rate directly,which needs to be extracted by numerical calculation.The differential operation commonly used in traditional methods can magnify the measurement error of the sensor,resulting in insufficient calculation accuracy of the line-of-sight angular rate.By analyzing the mathematical relationship between the missile-target relative motion and the angle tracking system,a multi-process-fusion integrated filter model of relative motion and angle tracking is presented.To overcome the defect that the infrared seeker cannot directly measure the missile-target distance,following the snake-hot-eye visual mechanism,a visual bionic imaging guidance method of estimating the missile-target relative distance from the infrared images is proposed to improve the observability of the filter model.Finally,target-tracking simulations verify that the estimation accuracy of target acceleration is improved by four times.

关键词： Roll-pitch semi-strapdown Infrared imaging seeker Target tracking Biological visual mechanism Snake hot eye Integrated filter

Target tracking method of Siamese networks based on the broad learning system

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 1043-1057页

作者： Dan Zhang C.L.Philip Chen Tieshan Li Yi Zuo Nguyen Quang Duy Navigation College Dalian Maritime UniversityDalianChina Innovation and Entrepreneurship Education College Dalian Minzu UniversityDalianChina Computer Science and Engineering College South China University of TechnologyGuangzhouChina Department of Computer and Information Science Faculty of Science and TechnologyUniversity of MacaoMacaoChina School of Automation Engineering University of Electronic Science and Technology of ChinaChengduChina Faculty of Navigation Vietnam Maritime UniversityHaiphongVietnam

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other aspects.However,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time tracking.tracking based on Siamese networks promotes the application of deep learning in the field of Target tracking,ensuring both accuracy and real‐time performance.However,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during tracking.Therefore,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online.The broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning ability.Aiming at the problems of Siamese networks and the characteristics of BLS,a Target tracking method based on BLS is proposed.The method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐detection.The broad re‐learning information is used for re‐detection when the Target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the adaptability.Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

关键词： broad learning system siamese network Target tracking

Adaptive Sampling for Near Space Hypersonic Gliding Target tracking

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Journal of Beijing Institute of Technology 2022年第6期31卷 584-594页

作者： Guanhua Ding Jinping Sun Ying Chen Juan Yu School of Electronics and Information Engineering Beihang UniversityBeijing 100191China Beijing Institute of Radio Measurement Beijing 100854China

For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering Target tracking applications.This paper presents a novel interactive multiple model(IMM)algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles(NSHGV)with a fast adaptive sam-pling control logic.The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers,thus achieving a balance between tracking accuracy and resource consumption.Simulation results on typical NSHGV targets show that the proposed algo-rithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.

关键词： near space hypersonic gliding vehicle(NSHGV) Target tracking adaptive sampling interactive multiple model(IMM)