A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracki...A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.展开更多
A kalman filter in the spherical-rectangular coordinate system to track a maneuvering target is introduced. A dynamic model with extended state vector is proposed in this paper to improve the state estimation (i.e. p...A kalman filter in the spherical-rectangular coordinate system to track a maneuvering target is introduced. A dynamic model with extended state vector is proposed in this paper to improve the state estimation (i.e. position, velocity and acceleration) even the sensor data(i.e. range, azimuth angle and elevation angle ) is color contaminated. The Kalman filter equations are decoupled by proper coordinate transformation and using filter gain rotation algorithm. Monto Carlo simulation is performed for different kinds of target trajectories(with the same measurement noise) and the root mean square values of estimation errors are computed. Results show that there is significant improvement in tracking capability over the methods discussed by other researchers.展开更多
To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm i...To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.展开更多
This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper cons...This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.展开更多
The problem of maneuvering for a servicing spacecraft(inspector)to inspect a noncooperative spacecraft(evader)in cislunar space is investigated in this paper.The evader,which may be a malfunctioning or uncontrolled sa...The problem of maneuvering for a servicing spacecraft(inspector)to inspect a noncooperative spacecraft(evader)in cislunar space is investigated in this paper.The evader,which may be a malfunctioning or uncontrolled satellite,introduces uncertainties due to its potential maneuvering capabilities.To address this challenge,the scenario is modeled as a special orbital game,incorporating the unique complexities of the cislunar environment.A variable-duration,turn-based inspection and anti-inspection game model is designed.The model defines both players'rules,constraints,and victory conditions,providing a framework for non-cooperative inspection.Strategies for both players are developed and validated based on their dynamical properties.The inspector's strategy integrates two-body Lambert transfers with shooting methods,while the evader's strategy aims to maximize the inspector's fuel consumption.Simulation results show that the evader's optimal strategy involves deliberate fluctuations in its lunar periapsis altitude,with the inspector's requiredΔV up to eight times greater than the evader's.The impact of game constraints is evaluated,and the effectiveness of deploying the inspector in low lunar orbit is compared with the inspector at the Earth-Moon Lagrange point L1.The strengths and weaknesses of both are shown.These findings provide valuable insights for future orbital servicing and orbital games.展开更多
Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative con...Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.展开更多
Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the ...Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.展开更多
The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is...The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is proposed to reduce communication load in networked control systems by redesigning existing anti-disturbance DAT algorithms and disturbance observers.Furthermore,a fully distributed event-triggering condition is employed to schedule event times for each agent.Simulation results demonstrate that the proposed ETAD-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of external disturbances,while the communication efficiency can be improved obviously.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Great Snipe(Gallinago media) is a shore bird which has a Near Threatened status on the global scale.However,little is known about its migration strategy from the breeding range in Russia.This study is the first one ai...Great Snipe(Gallinago media) is a shore bird which has a Near Threatened status on the global scale.However,little is known about its migration strategy from the breeding range in Russia.This study is the first one aiming to reveal migration routes,stopovers and wintering grounds of adult Great Snipes from their breeding range in Russia using GPS devices.We also analyzed connectivity of Great Snipes from different breeding populations of this species during non-breeding season.In 2021,we equipped seven males and three females with satellite transmitters,ICARUS Basic Tags,in the breeding range in central European Russia(56°75′N,37°65 E).One female appeared later in tundra of north-eastern Europe.In the second half of July to early September,birds migrated to Africa in a fairly wide front and made stopovers in Europe before crossing seas and the Sahara.Our data allowed to suppose high mortality of birds on migration,especially during the trans-Saharan flight.Only four Great Snipes reached Africa alive during southward migration.These birds spread over across wide area from Eritrea to Ghana after the trans-Saharan flight,after which they moved in a general westward direction and made final prolonged stopovers in Ghana or to the south of Chad Lake.In October/December birds relocated to wintering grounds in Sub-Equatorial Afrotropics as far as the south of Democratic Republic of the Congo and Zambia;with intermediate winter sites in low and middle reaches of the Congo Basin.Together with other published results,our data showed wide overlap of African non-breeding grounds of birds coming from lowland Eastern European and mountain Scandinavian breeding populations.The results also indicated insufficient conservation status of migration stopovers and wintering sites,used by Great Snipes,and demonstrated high importance of West Africa for conservation of this species.展开更多
To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneuvers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive...To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneuvers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is presented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter （UKF） via predicted variance Pk｜k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneuvering target compared with the standard UKF.展开更多
Radar Maneuvering Targets Tracking(RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal an...Radar Maneuvering Targets Tracking(RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal and data processing algorithm urgently to adapt the uncertainty of the environment. The mechanism of human cognition can help persons cope with the similar diffi-culties in visual tracking. Inspired by human cognition mechanism, a comprehensive method for RMTT is proposed. In the method, the model transition probability in Interacting Multiple Model(IMM) and the validation gate can be adjusted dynamically with target maneuver;the waveform in radar transmitter can vary with the perception of the environment. Experimental results in cluttered scenes show that the proposed algorithm is more accurate for perceiving the environment and targets, and the waveform selection algorithm is better than that with fixed waveform.展开更多
Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control proble...Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of taskdecomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.展开更多
To track the nonlinear,non-Gaussian bearings-only maneuvering target accurately online,the constrained auxiliary particle filtering(CAPF)algorithm is presented.To restrict the samples into the feasible area,the soft m...To track the nonlinear,non-Gaussian bearings-only maneuvering target accurately online,the constrained auxiliary particle filtering(CAPF)algorithm is presented.To restrict the samples into the feasible area,the soft measurement constraints are implemented into the update routine via the1 regularization.Meanwhile,to enhance the sampling diversity and efficiency,the target kinetic features and the latest observations are involved into the evolution.To take advantage of the past and the current measurement information simultaneously,the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors.As a result,the corresponding weights are more evenly distributed,and the posterior distribution of interest is approximated well with a heavier tailor.Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.展开更多
In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing ...In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association （IMMJPDA） filter in a relatively dense clutter environment.展开更多
The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear...The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter(CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method.The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the thirdorder term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF(FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions.展开更多
An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Sin...An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.展开更多
An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode p...An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering（UKF）.The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter.展开更多
The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to s...The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.展开更多
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
文摘A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.
文摘A kalman filter in the spherical-rectangular coordinate system to track a maneuvering target is introduced. A dynamic model with extended state vector is proposed in this paper to improve the state estimation (i.e. position, velocity and acceleration) even the sensor data(i.e. range, azimuth angle and elevation angle ) is color contaminated. The Kalman filter equations are decoupled by proper coordinate transformation and using filter gain rotation algorithm. Monto Carlo simulation is performed for different kinds of target trajectories(with the same measurement noise) and the root mean square values of estimation errors are computed. Results show that there is significant improvement in tracking capability over the methods discussed by other researchers.
文摘To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.
基金Supported by the Fundamental Research Funds for the Central Universities(2024ZYGXZR047)the National Natural Science Foundation of China(62373156)the Guangdong Basic and Applied Basic Research Foundation(2024A1515011736)。
文摘This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.
基金supported by the National Key R&D Pro-gram of China:Gravitational Wave Detection Project(Nos.2021YFC2026,2021YFC2202601,2021YFC2202603)the National Natural Science Foundation of China(Nos.12172288 and 12472046)。
文摘The problem of maneuvering for a servicing spacecraft(inspector)to inspect a noncooperative spacecraft(evader)in cislunar space is investigated in this paper.The evader,which may be a malfunctioning or uncontrolled satellite,introduces uncertainties due to its potential maneuvering capabilities.To address this challenge,the scenario is modeled as a special orbital game,incorporating the unique complexities of the cislunar environment.A variable-duration,turn-based inspection and anti-inspection game model is designed.The model defines both players'rules,constraints,and victory conditions,providing a framework for non-cooperative inspection.Strategies for both players are developed and validated based on their dynamical properties.The inspector's strategy integrates two-body Lambert transfers with shooting methods,while the evader's strategy aims to maximize the inspector's fuel consumption.Simulation results show that the evader's optimal strategy involves deliberate fluctuations in its lunar periapsis altitude,with the inspector's requiredΔV up to eight times greater than the evader's.The impact of game constraints is evaluated,and the effectiveness of deploying the inspector in low lunar orbit is compared with the inspector at the Earth-Moon Lagrange point L1.The strengths and weaknesses of both are shown.These findings provide valuable insights for future orbital servicing and orbital games.
基金supported by the Fundamental Research Funds for the Central Universities of China(FRF-TP-24-058A)with additional support from the National Key Laboratory of Helicopter Aeromechanics(2024-ZSJ-LB-02-02).
文摘Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.
基金funded by the Fundamental Research Funds for the Central Universities(Grant No.106-YDZX2025022)the Startup Foundation of New Professor at Nanjing Agricultural University(Grant No.106-804005)the“Qing Lan Project”of Jiangsu Higher Education Institutions.
文摘Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.
基金part supported by the National Natural Science Foundation(62203034,62273126,62203035)the Ling-Yan Research and Development Project of Zhejiang Province of China(2023C03185)。
文摘The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is proposed to reduce communication load in networked control systems by redesigning existing anti-disturbance DAT algorithms and disturbance observers.Furthermore,a fully distributed event-triggering condition is employed to schedule event times for each agent.Simulation results demonstrate that the proposed ETAD-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of external disturbances,while the communication efficiency can be improved obviously.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金A support for different activities in the framework of this research was provided by Max Planck Institute of Animal Behavior,the Russian space agency (Roskosmos),German Aerospace Center and Institute for Geography of the Russian Academy of Sciences via International Cooperation for Animal Research Using Space as well as by NABU–The Nature and Biodiversity Conservation Union2021 was carried out as a joint project of Birds Russia and Manfred-Hermsen Stiftung+2 种基金funded under state assignments of the Severtsov Institute of Ecology and Evolution of the Russian Academy of Science (No. 0089-2021-0004, FFER-2024-0013No. 0089-2021-0010, FFER-2024-0022No. 1022040700480-0-1.6.15)。
文摘Great Snipe(Gallinago media) is a shore bird which has a Near Threatened status on the global scale.However,little is known about its migration strategy from the breeding range in Russia.This study is the first one aiming to reveal migration routes,stopovers and wintering grounds of adult Great Snipes from their breeding range in Russia using GPS devices.We also analyzed connectivity of Great Snipes from different breeding populations of this species during non-breeding season.In 2021,we equipped seven males and three females with satellite transmitters,ICARUS Basic Tags,in the breeding range in central European Russia(56°75′N,37°65 E).One female appeared later in tundra of north-eastern Europe.In the second half of July to early September,birds migrated to Africa in a fairly wide front and made stopovers in Europe before crossing seas and the Sahara.Our data allowed to suppose high mortality of birds on migration,especially during the trans-Saharan flight.Only four Great Snipes reached Africa alive during southward migration.These birds spread over across wide area from Eritrea to Ghana after the trans-Saharan flight,after which they moved in a general westward direction and made final prolonged stopovers in Ghana or to the south of Chad Lake.In October/December birds relocated to wintering grounds in Sub-Equatorial Afrotropics as far as the south of Democratic Republic of the Congo and Zambia;with intermediate winter sites in low and middle reaches of the Congo Basin.Together with other published results,our data showed wide overlap of African non-breeding grounds of birds coming from lowland Eastern European and mountain Scandinavian breeding populations.The results also indicated insufficient conservation status of migration stopovers and wintering sites,used by Great Snipes,and demonstrated high importance of West Africa for conservation of this species.
基金supported by the National Natural Science Fundationof China(61102109)
文摘To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneuvers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is presented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter （UKF） via predicted variance Pk｜k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneuvering target compared with the standard UKF.
基金co-supported by the National Natural Science Foundation of China(No.61671453)the Anhui Province Natural Science Fund Project,China(No.1608085MF123)
文摘Radar Maneuvering Targets Tracking(RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal and data processing algorithm urgently to adapt the uncertainty of the environment. The mechanism of human cognition can help persons cope with the similar diffi-culties in visual tracking. Inspired by human cognition mechanism, a comprehensive method for RMTT is proposed. In the method, the model transition probability in Interacting Multiple Model(IMM) and the validation gate can be adjusted dynamically with target maneuver;the waveform in radar transmitter can vary with the perception of the environment. Experimental results in cluttered scenes show that the proposed algorithm is more accurate for perceiving the environment and targets, and the waveform selection algorithm is better than that with fixed waveform.
基金The authors would like to acknowledge National Natural Science Foundation of China(Grant No.61573285,No.62003267)Aeronautical Science Foundation of China(Grant No.2017ZC53021)+1 种基金Open Fund of Key Laboratory of Data Link Technology of China Electronics Technology Group Corporation(Grant No.CLDL-20182101)Natural Science Foundation of Shaanxi Province(Grant No.2020JQ-220)to provide fund for conducting experiments.
文摘Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of taskdecomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.
基金supported by the National Natural Science Foundation of China(61773267)the Shenzhen Fundamental Research Project(JCYJ2017030214551952420170818102503604)
文摘To track the nonlinear,non-Gaussian bearings-only maneuvering target accurately online,the constrained auxiliary particle filtering(CAPF)algorithm is presented.To restrict the samples into the feasible area,the soft measurement constraints are implemented into the update routine via the1 regularization.Meanwhile,to enhance the sampling diversity and efficiency,the target kinetic features and the latest observations are involved into the evolution.To take advantage of the past and the current measurement information simultaneously,the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors.As a result,the corresponding weights are more evenly distributed,and the posterior distribution of interest is approximated well with a heavier tailor.Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.
文摘In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association （IMMJPDA） filter in a relatively dense clutter environment.
基金supported by the National Natural Science Foundation of China (No. 61573283)
文摘The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter(CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method.The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the thirdorder term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF(FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions.
基金supported by the National Natural Science Foundation of China(6153102061471383)
文摘An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.
基金National Natural Science Foundation of China (60975028)National High-tech Research and Development Program (2009AA112203)+1 种基金Fundamental Research Funds for the Central Universities (CHD2009JC037)Natural Science Basic Research Plan in Shaanxi Province (2006F12)
文摘An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering（UKF）.The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter.
文摘The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.
基金Supported by the National Natural Science Foundation of China(No.61300214)the National Natural Science Foundation of Henan Province(No.132300410148)+1 种基金the Post-doctoral Science Foundation of China(No.2014M551999)the Funding Scheme of Young Key Teacher ofHenan Province Universities(No.2013GGJS-026)
文摘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.
