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
The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this...The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this paper proposed a high-maneuverability skipping motion strategy for the tandem twin-rotor AAV,inspired by the motion behavior of the flying fish to avoid aquatic and aerial predators near the water surface.The novel tandem twin-rotor AAV was employed as the research subject and a strategybased ADRC control method for validation,comparing it with a strategy-based PID control method.The results indicate that both control methods enable the designed AAV to achieve high stealth and maneuverability near the water surface with robust control stability.The strategy-based ADRC control method exhibits a certain advantage in controlling height,pitch angle,and reducing impact force.This motion strategy will offer an inspiring approach for the practical application of AAVs to some extent.展开更多
Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant ...Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant threats to SI,among which DDoS attack will intensify the erosion of limited bandwidth resources.Therefore,this paper proposes a DDoS attack tracking scheme using a multi-round iterative Viterbi algorithm to achieve high-accuracy attack path reconstruction and fast internal source locking,protecting SI from the source.Firstly,to reduce communication overhead,the logarithmic representation of the traffic volume is added to the digests after modeling SI,generating the lightweight deviation degree to construct the observation probability matrix for the Viterbi algorithm.Secondly,the path node matrix is expanded to multi-index matrices in the Viterbi algorithm to store index information for all probability values,deriving the path with non-repeatability and maximum probability.Finally,multiple rounds of iterative Viterbi tracking are performed locally to track DDoS attack based on trimming tracking results.Simulation and experimental results show that the scheme can achieve 96.8%tracking accuracy of external and internal DDoS attack at 2.5 seconds,with the communication overhead at 268KB/s,effectively protecting the limited bandwidth resources of SI.展开更多
Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this proble...Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this problem,the traditional particle tracking velocimetry method based on an optical flow was improved.The level set segmentation algorithm was used to obtain the boundary contour of the region with large velocity gradient changes,and the non-uniform flow field was divided into regions according to the boundary contour to obtain sub-regions with uniform velocity distribution.The particle tracking velocimetry method based on optical flow was used to measure the granular flow velocity in each sub-region,thus avoiding the problem of granular flow distribution.The simulation results show that the measurement accuracy of this method is approximately 10%higher than that of traditional methods.The method was applied to a velocity measurement experiment on dense granular flow in silos,and the velocity distribution of the granular flow was obtained,verifying the practicality of the method in granular flow fields.展开更多
To enhance the stability of helicopter maneuvers during task execution,a composite trajectory tracking controller design based on the implicit model(IM)and linear active disturbance rejection control(LADRC)is proposed...To enhance the stability of helicopter maneuvers during task execution,a composite trajectory tracking controller design based on the implicit model(IM)and linear active disturbance rejection control(LADRC)is proposed.Initially,aerodynamic models of the main and tail rotor are created using the blade element theory and the uniform inflow assumption.Subsequently,a comprehensive flight dynamic model of the helicopter is established through fitting aerodynamic force fitting.Subsequently,for precise helicopter maneuvering,including the spiral,spiral up,and Ranversman maneuver,a regular trim is undertaken,followed by minor perturbation linearization at the trim point.Utilizing the linearized model,controllers are created for the IM attitude inner loop and LADRC position outer loop of the helicopter.Ultimately,a comparison is made between the maneuver trajectory tracking results of the IM‑LADRC and the conventional proportional-integral-derivative(PID)control method is performed.Experimental results demonstrate that utilizing the post-trim minor perturbation linearized model in combination with the IM‑LADRC method can achieve higher precision in tracking results,thus enhancing the accuracy of helicopter maneuver execution.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
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
Based on the principle of statistical linear regression, a set of n ＋ 2 sigma points instead of 2n ＋ 1 sigma points used in the unscented Kalman filter （UKF）, is constructed to approximate the system state. And fi...Based on the principle of statistical linear regression, a set of n ＋ 2 sigma points instead of 2n ＋ 1 sigma points used in the unscented Kalman filter （UKF）, is constructed to approximate the system state. And filter accuracy is second order. Real-time of modified UKF is improved. In order to describe accurately the maneuvering target, the ＂current＂ statistical model is used. And the equation of acceleration error covariance is modified at every sample time of the filter. The modified adaptive UKF is presented for estimating the position, velocity and acceleration of maneuvering target. Monte Carlo simulations show the modified adaptive UKF acquires good performance for tracking position of maneuvering target. The modified adaptive UKF has better computational efficiency than UKF.展开更多
We explore the tracking problem of a maneuvering target. Tracking agents with third-order kinematics can com- municate with each other via wireless network. The communication network topology is arbitrary rather than ...We explore the tracking problem of a maneuvering target. Tracking agents with third-order kinematics can com- municate with each other via wireless network. The communication network topology is arbitrary rather than switches among several fixed topologies. The information sharing and interaction among agents are position, velocity, and accel- eration. Some sufficient conditions of tracking strategy have been proposed. Finally, a numerical example is employed to demonstrate the effectiveness of proposed tracking strategy.展开更多
Multiresolutional signal processing has been employed in image processing and computer vision to achieve improved performance that cannot be achieved using conventional signal processing techniques at only one resolut...Multiresolutional signal processing has been employed in image processing and computer vision to achieve improved performance that cannot be achieved using conventional signal processing techniques at only one resolution level [1,2,5,6] . In this paper,we have associated the thought of multiresolutional analysis with traditional Kalman filtering and proposed A new fusion algorithm based on singular Sensor and Multipale Models for maneuvering target tracking.展开更多
A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filte...A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.展开更多
Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short tr...Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles.To overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid beamforming.Accurately aligning the transmitter and receiver beams,i.e.beam training,is vitally important to high data rate transmission.However,it may cause huge overhead which has negative effects on initial access,handover,and tracking.Besides,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking latency.An efficient beam tracking method has a positive effect on sustaining robust links.This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile network.Especially,some open research problems are proposed to realize fast,accurate,and robust beam training and tracking.We hope that this survey provides guidelines for the researchers in the area of mmWave communications.展开更多
This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance system...This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.展开更多
Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board f...Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board field-programmable gate array(FPGA)with a core frequency of 100 MHz,our system facilitates reading and writing operations across 16 channels,performing discrete incremental proportional-integral-derivative(PID)calculations within 3.4 microseconds.Building upon this foundation,gradient and extremum algorithms are further integrated,incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm.This integration enhances the real-time performance and significantly increases the accuracy of atom tracking.Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite(HOPG)surface under room temperature atmospheric conditions.Through applying computer vision and image processing algorithms,atom tracking can be used when scanning a large area.The techniques primarily consist of two algorithms:the region of interest(ROI)-based feature matching algorithm,which achieves 97.92%accuracy,and the feature description-based matching algorithm,with an impressive 99.99%accuracy.Both implementation approaches have been tested for scanner drift measurements,and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.展开更多
Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating...Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.展开更多
This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a larg...This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.展开更多
An optical-tweezers-based dual-frequency-band particle tracking system was designed and fabricated for liquid viscositydetection. On the basis of the liquid viscosity dependent model of the particle’s restricted Brow...An optical-tweezers-based dual-frequency-band particle tracking system was designed and fabricated for liquid viscositydetection. On the basis of the liquid viscosity dependent model of the particle’s restricted Brownian motion with theFax´en correction taken into account, the liquid viscosity and optical trap stiffness were determined by fitting the theoreticalprediction with the measured power spectral densities of the particle’s displacement and velocity that were derived from thedual-frequency-band particle tracking data. When the SiO2 beads were employed as probe particles in the measurements ofdifferent kinds of liquids, the measurement results exhibit a good agreement with the reported results, as well as a detectionuncertainty better than 4.6%. This kind of noninvasive economical technique can be applied in diverse environments forboth in situ and ex situ viscosity detection of liquids.展开更多
This paper proposes an adaptive predefined-time terminal sliding mode control(APTSMC)scheme for attitude tracking control of a quadrotor.To create this,an adaptive predefined-time stability controller based on a termi...This paper proposes an adaptive predefined-time terminal sliding mode control(APTSMC)scheme for attitude tracking control of a quadrotor.To create this,an adaptive predefined-time stability controller based on a terminal sliding mode is constructed.The upper bound of convergence time in the proposed scheme can be adjusted by the explicit parameters during the design process of the controller.In addition,it is proved that the attitude tracking error will converge within two periods of the preset time.These two periods are set between two ranges:From the initial values to the sliding mode surface and from the sliding mode surface to the region near the origin.Furthermore,an adaptive law is adopted to eliminate unknown external disturbances and the effects of the uncertainties in the quadrotor model,so it is unnecessary to require the prior knowledge of the upper bound of the perturbations.Simulation results are produced and comparative case studies are carried out to demonstrate that the proposed scheme has faster convergence speed and smaller tracking errors.展开更多
In this paper,the fixed-time time-varying formation of heterogeneous multi-agent systems(MASs) based on tracking error observer under denial-of-service(DoS) attacks is investigated.Firstly,the dynamic pinning strategy...In this paper,the fixed-time time-varying formation of heterogeneous multi-agent systems(MASs) based on tracking error observer under denial-of-service(DoS) attacks is investigated.Firstly,the dynamic pinning strategy is used to reconstruct the communication channel for the system that suffers from DoS attacks to prevent the discontinuous transmission information of the communication network from affecting MASs formation.Then,considering that the leader state is not available to each follower under DoS attacks,a fixed-time distributed observer without velocity information is constructed to estimate the tracking error between followers and the leader.Finally,adaptive radial basis function neural network(RBFNN) is used to approximate the unknown ensemble disturbances in the system,and the fixed-time time-varying formation scheme is designed with the constructed observer.The effectiveness of the proposed control algorithm is demonstrated by the numerical simulation.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.
基金supported by Southern Marine Science and Guangdong Laboratory(Zhuhai)(Grant No.SML2023SP229)。
文摘The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this paper proposed a high-maneuverability skipping motion strategy for the tandem twin-rotor AAV,inspired by the motion behavior of the flying fish to avoid aquatic and aerial predators near the water surface.The novel tandem twin-rotor AAV was employed as the research subject and a strategybased ADRC control method for validation,comparing it with a strategy-based PID control method.The results indicate that both control methods enable the designed AAV to achieve high stealth and maneuverability near the water surface with robust control stability.The strategy-based ADRC control method exhibits a certain advantage in controlling height,pitch angle,and reducing impact force.This motion strategy will offer an inspiring approach for the practical application of AAVs to some extent.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1005000)the National Natural Science Foundation of China(Grant No.62025110 and 62101308).
文摘Satellite Internet(SI)provides broadband access as a critical information infrastructure in 6G.However,with the integration of the terrestrial Internet,the influx of massive terrestrial traffic will bring significant threats to SI,among which DDoS attack will intensify the erosion of limited bandwidth resources.Therefore,this paper proposes a DDoS attack tracking scheme using a multi-round iterative Viterbi algorithm to achieve high-accuracy attack path reconstruction and fast internal source locking,protecting SI from the source.Firstly,to reduce communication overhead,the logarithmic representation of the traffic volume is added to the digests after modeling SI,generating the lightweight deviation degree to construct the observation probability matrix for the Viterbi algorithm.Secondly,the path node matrix is expanded to multi-index matrices in the Viterbi algorithm to store index information for all probability values,deriving the path with non-repeatability and maximum probability.Finally,multiple rounds of iterative Viterbi tracking are performed locally to track DDoS attack based on trimming tracking results.Simulation and experimental results show that the scheme can achieve 96.8%tracking accuracy of external and internal DDoS attack at 2.5 seconds,with the communication overhead at 268KB/s,effectively protecting the limited bandwidth resources of SI.
文摘Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this problem,the traditional particle tracking velocimetry method based on an optical flow was improved.The level set segmentation algorithm was used to obtain the boundary contour of the region with large velocity gradient changes,and the non-uniform flow field was divided into regions according to the boundary contour to obtain sub-regions with uniform velocity distribution.The particle tracking velocimetry method based on optical flow was used to measure the granular flow velocity in each sub-region,thus avoiding the problem of granular flow distribution.The simulation results show that the measurement accuracy of this method is approximately 10%higher than that of traditional methods.The method was applied to a velocity measurement experiment on dense granular flow in silos,and the velocity distribution of the granular flow was obtained,verifying the practicality of the method in granular flow fields.
基金supported in part by the National Natural Science Foundation of China(No.12032012)the Key Discipline Construction Project of Colleges and Universities in Jiangsu Province.
文摘To enhance the stability of helicopter maneuvers during task execution,a composite trajectory tracking controller design based on the implicit model(IM)and linear active disturbance rejection control(LADRC)is proposed.Initially,aerodynamic models of the main and tail rotor are created using the blade element theory and the uniform inflow assumption.Subsequently,a comprehensive flight dynamic model of the helicopter is established through fitting aerodynamic force fitting.Subsequently,for precise helicopter maneuvering,including the spiral,spiral up,and Ranversman maneuver,a regular trim is undertaken,followed by minor perturbation linearization at the trim point.Utilizing the linearized model,controllers are created for the IM attitude inner loop and LADRC position outer loop of the helicopter.Ultimately,a comparison is made between the maneuver trajectory tracking results of the IM‑LADRC and the conventional proportional-integral-derivative(PID)control method is performed.Experimental results demonstrate that utilizing the post-trim minor perturbation linearized model in combination with the IM‑LADRC method can achieve higher precision in tracking results,thus enhancing the accuracy of helicopter maneuver execution.
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.
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the National Natural Science Foundation of China (413090503)
文摘Based on the principle of statistical linear regression, a set of n ＋ 2 sigma points instead of 2n ＋ 1 sigma points used in the unscented Kalman filter （UKF）, is constructed to approximate the system state. And filter accuracy is second order. Real-time of modified UKF is improved. In order to describe accurately the maneuvering target, the ＂current＂ statistical model is used. And the equation of acceleration error covariance is modified at every sample time of the filter. The modified adaptive UKF is presented for estimating the position, velocity and acceleration of maneuvering target. Monte Carlo simulations show the modified adaptive UKF acquires good performance for tracking position of maneuvering target. The modified adaptive UKF has better computational efficiency than UKF.
基金Project supported by the National Natural Science Foundation of China(Grant No.61104086)the Scientific Research and Postgraduate Training Joint-Build Project of China(Grant No.20120639002)
文摘We explore the tracking problem of a maneuvering target. Tracking agents with third-order kinematics can com- municate with each other via wireless network. The communication network topology is arbitrary rather than switches among several fixed topologies. The information sharing and interaction among agents are position, velocity, and accel- eration. Some sufficient conditions of tracking strategy have been proposed. Finally, a numerical example is employed to demonstrate the effectiveness of proposed tracking strategy.
文摘Multiresolutional signal processing has been employed in image processing and computer vision to achieve improved performance that cannot be achieved using conventional signal processing techniques at only one resolution level [1,2,5,6] . In this paper,we have associated the thought of multiresolutional analysis with traditional Kalman filtering and proposed A new fusion algorithm based on singular Sensor and Multipale Models for maneuvering target tracking.
基金supported by National Natural Science Foundation of China (Nos.62265010,62061024)Gansu Province Science and Technology Plan (No.23YFGA0062)Gansu Province Innovation Fund (No.2022A-215)。
文摘A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 92267202in part by the Municipal Government of Quzhou under Grant 2023D027+2 种基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62321001in part by the National Key Research and Development Program of China under Grant 2020YFA0711303in part by the Beijing Natural Science Foundation under Grant Z220004.
文摘Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles.To overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid beamforming.Accurately aligning the transmitter and receiver beams,i.e.beam training,is vitally important to high data rate transmission.However,it may cause huge overhead which has negative effects on initial access,handover,and tracking.Besides,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking latency.An efficient beam tracking method has a positive effect on sustaining robust links.This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile network.Especially,some open research problems are proposed to realize fast,accurate,and robust beam training and tracking.We hope that this survey provides guidelines for the researchers in the area of mmWave communications.
基金the National Natural Science Foundation of China(Grant No.12072090).
文摘This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.
基金Project supported by the National Science Fund for Distinguished Young Scholars(Grant No.T2125014)the Special Fund for Research on National Major Research Instruments of the National Natural Science Foundation of China(Grant No.11927808)the CAS Key Technology Research and Development Team Project(Grant No.GJJSTD20200005)。
文摘Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board field-programmable gate array(FPGA)with a core frequency of 100 MHz,our system facilitates reading and writing operations across 16 channels,performing discrete incremental proportional-integral-derivative(PID)calculations within 3.4 microseconds.Building upon this foundation,gradient and extremum algorithms are further integrated,incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm.This integration enhances the real-time performance and significantly increases the accuracy of atom tracking.Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite(HOPG)surface under room temperature atmospheric conditions.Through applying computer vision and image processing algorithms,atom tracking can be used when scanning a large area.The techniques primarily consist of two algorithms:the region of interest(ROI)-based feature matching algorithm,which achieves 97.92%accuracy,and the feature description-based matching algorithm,with an impressive 99.99%accuracy.Both implementation approaches have been tested for scanner drift measurements,and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.
基金supported in part by the National Key Research and Development Program of China under Grant 2018YFA0702501in part by NSFC under Grant 41974126,41674116 and 42004101。
文摘Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.
基金supported in part by the National Key R&D Program of China under Grant 2021YFB2011300the National Natural Science Foundation of China under Grant 52075262。
文摘This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.
基金the National NaturalScience Foundation of China (Grant No. 62175135)theSpecial Foundation of Local Scientific and TechnologicalDevelopment Guided by Central Government (GrantNo. YDZJSX20231A006)the Fundamental ResearchProgram of Shanxi Province (Grant No. 202103021224025).
文摘An optical-tweezers-based dual-frequency-band particle tracking system was designed and fabricated for liquid viscositydetection. On the basis of the liquid viscosity dependent model of the particle’s restricted Brownian motion with theFax´en correction taken into account, the liquid viscosity and optical trap stiffness were determined by fitting the theoreticalprediction with the measured power spectral densities of the particle’s displacement and velocity that were derived from thedual-frequency-band particle tracking data. When the SiO2 beads were employed as probe particles in the measurements ofdifferent kinds of liquids, the measurement results exhibit a good agreement with the reported results, as well as a detectionuncertainty better than 4.6%. This kind of noninvasive economical technique can be applied in diverse environments forboth in situ and ex situ viscosity detection of liquids.
文摘This paper proposes an adaptive predefined-time terminal sliding mode control(APTSMC)scheme for attitude tracking control of a quadrotor.To create this,an adaptive predefined-time stability controller based on a terminal sliding mode is constructed.The upper bound of convergence time in the proposed scheme can be adjusted by the explicit parameters during the design process of the controller.In addition,it is proved that the attitude tracking error will converge within two periods of the preset time.These two periods are set between two ranges:From the initial values to the sliding mode surface and from the sliding mode surface to the region near the origin.Furthermore,an adaptive law is adopted to eliminate unknown external disturbances and the effects of the uncertainties in the quadrotor model,so it is unnecessary to require the prior knowledge of the upper bound of the perturbations.Simulation results are produced and comparative case studies are carried out to demonstrate that the proposed scheme has faster convergence speed and smaller tracking errors.
文摘In this paper,the fixed-time time-varying formation of heterogeneous multi-agent systems(MASs) based on tracking error observer under denial-of-service(DoS) attacks is investigated.Firstly,the dynamic pinning strategy is used to reconstruct the communication channel for the system that suffers from DoS attacks to prevent the discontinuous transmission information of the communication network from affecting MASs formation.Then,considering that the leader state is not available to each follower under DoS attacks,a fixed-time distributed observer without velocity information is constructed to estimate the tracking error between followers and the leader.Finally,adaptive radial basis function neural network(RBFNN) is used to approximate the unknown ensemble disturbances in the system,and the fixed-time time-varying formation scheme is designed with the constructed observer.The effectiveness of the proposed control algorithm is demonstrated by the numerical simulation.
