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
The multi-armored target tracking(MATT)plays a crucial role in coordinated tracking and strike.The occlusion and insertion among targets and target scale variation is the key problems in MATT.Most stateof-the-art mult...The multi-armored target tracking(MATT)plays a crucial role in coordinated tracking and strike.The occlusion and insertion among targets and target scale variation is the key problems in MATT.Most stateof-the-art multi-object tracking(MOT)works adopt the tracking-by-detection strategy,which rely on compute-intensive sliding window or anchoring scheme in detection module and neglect the target scale variation in tracking module.In this work,we proposed a more efficient and effective spatial-temporal attention scheme to track multi-armored target in the ground battlefield.By simulating the structure of the retina,a novel visual-attention Gabor filter branch is proposed to enhance detection.By introducing temporal information,some online learned target-specific Convolutional Neural Networks(CNNs)are adopted to address occlusion.More importantly,we built a MOT dataset for armored targets,called Armored Target Tracking dataset(ATTD),based on which several comparable experiments with state-ofthe-art methods are conducted.Experimental results show that the proposed method achieves outstanding tracking performance and meets the actual application requirements.展开更多
We propose a target tracking method based on particle filtering(PF) to solve the nonlinear non-Gaussian target-tracking problem in the bistatic radar systems using external radiation sources. Traditional nonlinear sta...We propose a target tracking method based on particle filtering(PF) to solve the nonlinear non-Gaussian target-tracking problem in the bistatic radar systems using external radiation sources. Traditional nonlinear state estimation method is extended Kalman filtering (EKF), which is to do the first level Taylor series extension. It will cause an inaccuracy or even a scatter estimation result on condition that there is either a highly nonlinear target or a large noise square-error. Besides, Kalman filtering is the optimal resolution under a Gaussian noise assumption, and is not suitable to the non-Gaussian condition. PF is a sort of statistic filtering based on Monte Carlo simulation that is using some random samples (particles) to simulate the posterior probability density of system random variables. This method can be used in any nonlinear random system. It can be concluded through simulation that PF can achieve higher accuracy than the traditional EKF.展开更多
The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and mu...The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n＋ 2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions.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
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance....To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.展开更多
A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically...A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.展开更多
In this paper, a novel object tracking based on a particle filter and speeded up robust feature （SURF） is proposed, which uses both color and SURF features. The SURF feature makes the tracking result more robust. On...In this paper, a novel object tracking based on a particle filter and speeded up robust feature （SURF） is proposed, which uses both color and SURF features. The SURF feature makes the tracking result more robust. On the other hand, the particle selection can lead to save time. In addition, we also consider the matched particle applicable to calculating the SURF weight. Owing to the color, spatial, and SURF features being adopted, this method is more robust than the traditional color-based appearance model. Experimental results demonstrate the robustness and accurate tracking results with challenging sequences. Besides, the proposed method outperforms other methods during the intersection of similar color and object＇s partial occlusion.展开更多
Tracking moving wideband sound sources is one of the most challenging issues in the acoustic array signal processing which is based on the direction of arrival(DOA) estimation. Compressive sensing(CS) is a recent theo...Tracking moving wideband sound sources is one of the most challenging issues in the acoustic array signal processing which is based on the direction of arrival(DOA) estimation. Compressive sensing(CS) is a recent theory exploring the signal sparsity representation, which has been proved to be superior for the DOA estimation. However, the spatial aliasing and the offset at endfire are the main obstacles for CS applied in the wideband DOA estimation. We propose a particle filter based compressive sensing method for tracking moving wideband sound sources. First, the initial DOA estimates are obtained by wideband CS algorithms. Then, the real sources are approximated by a set of particles with different weights assigned. The kernel density estimator is used as the likelihood function of particle filter. We present the results for both uniform and random linear array. Simulation results show that the spatial aliasing is disappeared and the offset at endfire is reduced. We show that the proposed method can achieve satisfactory tracking performance regardless of using uniform or random linear array.展开更多
In order to solve problems in high dynamic environment, a frequency-locked loop （FLL） assisted phase-locked loop （PLL） is put forward for carrier tracking. On the basis of the analysis of discriminators, the total...In order to solve problems in high dynamic environment, a frequency-locked loop （FLL） assisted phase-locked loop （PLL） is put forward for carrier tracking. On the basis of the analysis of discriminators, the total phase error of the tracking loop is analyzed and a general error expression is derived. By using linearization and Jaffe-Rechtin coefficients, the performance of a special first order FLL-assisted second order PLL is analyzed to get a closed expression. Analysis results and simula- tions show that there exist an optimal FLL loop bandwidth and a optimal PLL loop bandwidth which can make the phase jitter much less than that when the PLL is used alone.展开更多
Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and ...Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.展开更多
In this paper,a Millimeter wave(mmWave)beam tracking problem is studied in orthogonal time frequency space(OTFS)systems.Considering the nonlinearity of beamforming and the constraints of existing Kalman-filtering base...In this paper,a Millimeter wave(mmWave)beam tracking problem is studied in orthogonal time frequency space(OTFS)systems.Considering the nonlinearity of beamforming and the constraints of existing Kalman-filtering based beam tracking schemes,we propose a novel Cubature Kalman Filter(CKF)framework tracking the channel state information(CSI)to manage the challenge of highspeed channel variation in single-user moving scene for OTFS systems.Aiming for low complexity for mobile settings,this paper trains only one beam pair to track a path to maintain the reliable communication link in the analog beamforming architecture.Simulation results show that our proposed method has better tracking performance to improve the accuracy of the estimated beam angle compared with prior work.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
Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estima...Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.展开更多
Dual-frequency satellite positioning receivers are widely used because they can eliminate ionospheric delay and solve the full-circumference ambiguity quickly.However,in traditional dual-frequency receivers,the releva...Dual-frequency satellite positioning receivers are widely used because they can eliminate ionospheric delay and solve the full-circumference ambiguity quickly.However,in traditional dual-frequency receivers,the relevance of dual-frequency signals are not considered,and,with no improvement imposed to the tracking loop,two independent tracking loops are used to achieve the tracking of dual-frequency signals.In this paper,the Bei Dou dual-frequency signals joint tracking algorithm based on Kalman filter is proposed for the tracking of Bei Dou B1I and B3I dual-frequency signals.Taking the relevance of B1I and B3I signals into consideration,the algorithm adds a Kalman filter between the phase detector and carrier loop filter of the traditional dual-frequency independent tracking loop.The output results of the phase detectors of the B1I and B3I branches are then combined and filtered by the Kalman filter,and the results are input to the carrier loop filters of the corresponding branches.Proved by experiments,the algorithm not only enables the loop to enter a stable tracking state quickly,but also reduces the noise bandwidth of the two loop filters by about 10 Hz with the same tracking performance obtained.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
The true-time delay(TTD)units are critical for solving beam squint and frequency selective fading inWideband Large-Scale Antenna Systems(LSASs).In this work,we propose a TTD array architecture for wideband multi-beam ...The true-time delay(TTD)units are critical for solving beam squint and frequency selective fading inWideband Large-Scale Antenna Systems(LSASs).In this work,we propose a TTD array architecture for wideband multi-beam tracking that eliminates the beam squint phenomenon and filters out interference signals by applying a spatial filter and time delay estimations(TDEs).The paper presents a novel approach to spatial filter design by introducing a transformation matrix that can optimize the beam response in a specific direction and at a specific frequency.Using the variable fractional delay(VFD)filters,we propose a TDE algorithm with a Newton-Raphson iteration update process that corrects the arrival time delay difference between sensors.Simulations and examples have demonstrated that the proposed architecture can achieve beam tracking within 10 ms at the low signalto-noise ratio(SNR)and demodulation loss is less than 0.5 dB in wideband multi-beam scenarios.展开更多
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
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This work was supported by the National Key Research and Development Program of China(No.2016YFC0802904)National Natural Science Foundation of China(No.61671470)+1 种基金Natural Science Foundation of Jiangsu Province(BK20161470)62nd batch of funded projects of China Postdoctoral Science Foundation(No.2017M623423).
文摘The multi-armored target tracking(MATT)plays a crucial role in coordinated tracking and strike.The occlusion and insertion among targets and target scale variation is the key problems in MATT.Most stateof-the-art multi-object tracking(MOT)works adopt the tracking-by-detection strategy,which rely on compute-intensive sliding window or anchoring scheme in detection module and neglect the target scale variation in tracking module.In this work,we proposed a more efficient and effective spatial-temporal attention scheme to track multi-armored target in the ground battlefield.By simulating the structure of the retina,a novel visual-attention Gabor filter branch is proposed to enhance detection.By introducing temporal information,some online learned target-specific Convolutional Neural Networks(CNNs)are adopted to address occlusion.More importantly,we built a MOT dataset for armored targets,called Armored Target Tracking dataset(ATTD),based on which several comparable experiments with state-ofthe-art methods are conducted.Experimental results show that the proposed method achieves outstanding tracking performance and meets the actual application requirements.
文摘We propose a target tracking method based on particle filtering(PF) to solve the nonlinear non-Gaussian target-tracking problem in the bistatic radar systems using external radiation sources. Traditional nonlinear state estimation method is extended Kalman filtering (EKF), which is to do the first level Taylor series extension. It will cause an inaccuracy or even a scatter estimation result on condition that there is either a highly nonlinear target or a large noise square-error. Besides, Kalman filtering is the optimal resolution under a Gaussian noise assumption, and is not suitable to the non-Gaussian condition. PF is a sort of statistic filtering based on Monte Carlo simulation that is using some random samples (particles) to simulate the posterior probability density of system random variables. This method can be used in any nonlinear random system. It can be concluded through simulation that PF can achieve higher accuracy than the traditional EKF.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60971104)the Fundamental Research Funds for the Cental Universities (Grant No. SWJTU09BR092)the Young Teacher Scientific Research Foundation of Southwest Jiaotong University (Grant No. 2009Q032)
文摘The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n＋ 2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions.
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.
文摘To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
基金Supported by the National Natural Science Foundation of China(6160303040,61433003)Yunnan Applied Basic Research Project of China(201701CF00037)Yunnan Provincial Science and Technology Department Key Research Program(Engineering)(2018BA070)
文摘A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.
基金supported by the NSC under Grant No.NSC101-2221-E-259-032-MY3
文摘In this paper, a novel object tracking based on a particle filter and speeded up robust feature （SURF） is proposed, which uses both color and SURF features. The SURF feature makes the tracking result more robust. On the other hand, the particle selection can lead to save time. In addition, we also consider the matched particle applicable to calculating the SURF weight. Owing to the color, spatial, and SURF features being adopted, this method is more robust than the traditional color-based appearance model. Experimental results demonstrate the robustness and accurate tracking results with challenging sequences. Besides, the proposed method outperforms other methods during the intersection of similar color and object＇s partial occlusion.
基金supported by the NFSC Grants 51375385 and 51675425Natural Science Basic Research Plan in Shaanxi Province of China Grants 2016JZ013
文摘Tracking moving wideband sound sources is one of the most challenging issues in the acoustic array signal processing which is based on the direction of arrival(DOA) estimation. Compressive sensing(CS) is a recent theory exploring the signal sparsity representation, which has been proved to be superior for the DOA estimation. However, the spatial aliasing and the offset at endfire are the main obstacles for CS applied in the wideband DOA estimation. We propose a particle filter based compressive sensing method for tracking moving wideband sound sources. First, the initial DOA estimates are obtained by wideband CS algorithms. Then, the real sources are approximated by a set of particles with different weights assigned. The kernel density estimator is used as the likelihood function of particle filter. We present the results for both uniform and random linear array. Simulation results show that the spatial aliasing is disappeared and the offset at endfire is reduced. We show that the proposed method can achieve satisfactory tracking performance regardless of using uniform or random linear array.
文摘In order to solve problems in high dynamic environment, a frequency-locked loop （FLL） assisted phase-locked loop （PLL） is put forward for carrier tracking. On the basis of the analysis of discriminators, the total phase error of the tracking loop is analyzed and a general error expression is derived. By using linearization and Jaffe-Rechtin coefficients, the performance of a special first order FLL-assisted second order PLL is analyzed to get a closed expression. Analysis results and simula- tions show that there exist an optimal FLL loop bandwidth and a optimal PLL loop bandwidth which can make the phase jitter much less than that when the PLL is used alone.
基金Supported by the National Natural Science Foundation of China(61701029)
文摘Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.
文摘In this paper,a Millimeter wave(mmWave)beam tracking problem is studied in orthogonal time frequency space(OTFS)systems.Considering the nonlinearity of beamforming and the constraints of existing Kalman-filtering based beam tracking schemes,we propose a novel Cubature Kalman Filter(CKF)framework tracking the channel state information(CSI)to manage the challenge of highspeed channel variation in single-user moving scene for OTFS systems.Aiming for low complexity for mobile settings,this paper trains only one beam pair to track a path to maintain the reliable communication link in the analog beamforming architecture.Simulation results show that our proposed method has better tracking performance to improve the accuracy of the estimated beam angle compared with prior work.
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Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.
基金supported by the National Natural Science Foundation of China (No.51505221)the Nanjing University of Aeronautics and Astronautics Graduate Innovation Base (Lab) Open Fund (No.kfjj20190312)
文摘Dual-frequency satellite positioning receivers are widely used because they can eliminate ionospheric delay and solve the full-circumference ambiguity quickly.However,in traditional dual-frequency receivers,the relevance of dual-frequency signals are not considered,and,with no improvement imposed to the tracking loop,two independent tracking loops are used to achieve the tracking of dual-frequency signals.In this paper,the Bei Dou dual-frequency signals joint tracking algorithm based on Kalman filter is proposed for the tracking of Bei Dou B1I and B3I dual-frequency signals.Taking the relevance of B1I and B3I signals into consideration,the algorithm adds a Kalman filter between the phase detector and carrier loop filter of the traditional dual-frequency independent tracking loop.The output results of the phase detectors of the B1I and B3I branches are then combined and filtered by the Kalman filter,and the results are input to the carrier loop filters of the corresponding branches.Proved by experiments,the algorithm not only enables the loop to enter a stable tracking state quickly,but also reduces the noise bandwidth of the two loop filters by about 10 Hz with the same tracking performance obtained.
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supported by the foundation of National Key Laboratory of Electromagnetic Environment(Grant No.202103012).
文摘The true-time delay(TTD)units are critical for solving beam squint and frequency selective fading inWideband Large-Scale Antenna Systems(LSASs).In this work,we propose a TTD array architecture for wideband multi-beam tracking that eliminates the beam squint phenomenon and filters out interference signals by applying a spatial filter and time delay estimations(TDEs).The paper presents a novel approach to spatial filter design by introducing a transformation matrix that can optimize the beam response in a specific direction and at a specific frequency.Using the variable fractional delay(VFD)filters,we propose a TDE algorithm with a Newton-Raphson iteration update process that corrects the arrival time delay difference between sensors.Simulations and examples have demonstrated that the proposed architecture can achieve beam tracking within 10 ms at the low signalto-noise ratio(SNR)and demodulation loss is less than 0.5 dB in wideband multi-beam scenarios.
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.
