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
Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction.However,due to the high nonlinearity of eye motion,how to ensure the robustness of external interfe...Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction.However,due to the high nonlinearity of eye motion,how to ensure the robustness of external interference and accuracy of eye tracking pose the primary obstacle to the integration of eye movements into today＇s interfaces.In this paper,we present a strong tracking unscented Kalman filter （ST-UKF） algorithm,aiming to overcome the difficulty in nonlinear eye tracking.In the proposed ST-UKF,the Suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking.Compared with the related Kalman filter for eye tracking,the proposed ST-UKF has potential advantages in robustness and tracking accuracy.The last experimental results show the validity of our method for eye tracking under realistic conditions.展开更多
At present,current filters can basically solve the filtering problem in target tracking,but there are still many problems such as too many filtering variants,too many filtering forms,loosely coupled with the target mo...At present,current filters can basically solve the filtering problem in target tracking,but there are still many problems such as too many filtering variants,too many filtering forms,loosely coupled with the target motion model,and so on.To solve the above problems,we carry out crossapplication research of artificial intelligence theory and methods in the field of tracking filters.We firstly analyze the computation graphs of typical a-βand Kalman.Through analysis,it is concluded that a-βand Kalman have the same computation structures analogous to a typical recurrent neural network and can be considered as a kind of recurrent neural network with constrained weights.Then,given this and considering that a recurrent neural network has the recognition capability for target motion patterns,a new filter is developed in a unified neural network architecture and specifically constructed using feedforward neural network,recurrent neural network,and attention mechanism.And the unified tracking filter proposed in this paper can generate three aspects of unity:a unified target motion model,an adaptive filter method,and an overall track filtering framework.Finally,Simulation results show that the proposed filter is effective and useful,of which the overall performance is superior to those of compared filters.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
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
In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even ped...In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.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
Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method ...Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method is confined to the expensive hardware equipments.This paper starts from Gabor transform and applies the Gabor time-frequency filtering to vibration signal.The order component＇s time-frequency coefficients are extracted by mask operation.The order component is reconstructed from the obtained coefficients.The following four key technologies,such as smoothing rotary speed curve,defining filtering band width,constructing the mask operation matrix and reconstructing signal component,are also deeply discussed.Moreover,the technique to smooth the rotary speed curve based on polynomial approximation,the method to determine filtering band width,the arithmetic to constitute mask array and the iterative algorithm to reconstruct signal based on minimum mean square error are specifically analyzed.The 4th order component is successfully gained by using the methods that Gabor time-frequency filter,and the validity and feasibility of this method are approved.This method can solve the problem of order tracking filter technologies which used to depend on hardware and efficiently improve the accuracy of order analysis.展开更多
Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is aut...Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio（SNR） is unknown.If the frequency-locked loop（FLL） or the phase-locked loop（PLL） with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused.Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence.Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio.The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process.In addition, the upper diagonal（UD） factorization and innovation adaptive control are used to reduce model estimation errors,suppress filter divergence and improve filtering accuracy.The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect.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
When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competitio...When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.展开更多
As a new method for dealing with any nonlinear or non-Ganssian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters （PF） are favored by researchers and widely applied in many fiel...As a new method for dealing with any nonlinear or non-Ganssian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters （PF） are favored by researchers and widely applied in many fields. Based on particle filtering, an improved extended Kalman filter （EKF） proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for target tracking. Performances of the EKF, basic PF and the improved PF are compared in target tracking examples. The simulation results confirm that the improved particle filter outperforms the others.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
A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function...A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation, eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses, in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions. The last experimental results show that the proposed method not only improves the robustness for nonlinear eye tracking, but also can provide more accurate estimation than the traditional Kalman filter.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
For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter （EKF） proposal function is applied to ...For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter （EKF） proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo （MCMC） method, the resampling step, ere novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.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
A new improved particle filter algorithm with the simplified UT （unscented transformation） and the modified unscented Kalman filter （UKF） proposal distribution is presented. The scaling factor is added to adaptive...A new improved particle filter algorithm with the simplified UT （unscented transformation） and the modified unscented Kalman filter （UKF） proposal distribution is presented. The scaling factor is added to adaptively estimate on line and to improve the filtering performance. An adaptive algorithm is developed. In the bearings-only tracking experiments, the results confirm the improved particle filter algorithm outperforms others.展开更多
This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot （2009） using a WRF-based ensemble Kalman filter （EnKF） data assimilation （DA） system. Th...This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot （2009） using a WRF-based ensemble Kalman filter （EnKF） data assimilation （DA） system. The results showed that the performance of radar EnKF DA was quite sensitive to the number of radars being assimilated and the DA timing relative to the landfall of the tropical cyclone （TC）. It was found that assimilating radial velocity （Vr） data from all the four operational radars during the 6 h immediately before TC landfall was quite important for the track and rainfall forecasts after the TC made landfall. The TC track forecast error could be decreased by about 43% and the 24-h rainfall forecast skill could be almost tripled. Assimilating Vr data from a single radar outperformed the experiment without DA, though with less improvement compared to the multiple-radar DA experiment. Different forecast performances were obtained by assimilating different radars, which was closely related to the first-time wind analysis increment, the location of moisture transport, the quasi-stationary rainband, and the local convergence line. However, only assimilating Vr data when the TC was farther away from making landfall might worsen TC track and rainfall forecasts. Besides, this work also demonstrated that Vr data from multiple radars, instead of a single radar, should be used for verification to obtain a more reliable assessment of the EnKF performance.展开更多
To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is intro...To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is introduced and a fuzzy adaptive strong tracking cubature Kalman filter( FASTCKF) based on fuzzy logic controller is proposed. This method monitors residual absolute mean and standard deviation of each measurement component with fuzzy logic adaptive controller( FLAC),and adjusts the softening factor matrix dynamically by fuzzy rules,which is capable to modify suboptimal fading factor of STF adaptively and improve the filter's robust adaptive capacity. The simulation results show that the improved filtering performance is superior to the conventional square root cubature Kalman filter( SCKF) and the strong tracking square root cubature Kalman filter( STSCKF).展开更多
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(No.60971104)the Program for New Century Excellent Talents in University of China(No.NCET-05-0794)the Young Teacher Scientific Research Foundation of Southwest Jiaotong University(No.2009Q032)
文摘Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction.However,due to the high nonlinearity of eye motion,how to ensure the robustness of external interference and accuracy of eye tracking pose the primary obstacle to the integration of eye movements into today＇s interfaces.In this paper,we present a strong tracking unscented Kalman filter （ST-UKF） algorithm,aiming to overcome the difficulty in nonlinear eye tracking.In the proposed ST-UKF,the Suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking.Compared with the related Kalman filter for eye tracking,the proposed ST-UKF has potential advantages in robustness and tracking accuracy.The last experimental results show the validity of our method for eye tracking under realistic conditions.
基金supported by the National Natural Science Foundation of China(Nos.61790554 and 62001499)。
文摘At present,current filters can basically solve the filtering problem in target tracking,but there are still many problems such as too many filtering variants,too many filtering forms,loosely coupled with the target motion model,and so on.To solve the above problems,we carry out crossapplication research of artificial intelligence theory and methods in the field of tracking filters.We firstly analyze the computation graphs of typical a-βand Kalman.Through analysis,it is concluded that a-βand Kalman have the same computation structures analogous to a typical recurrent neural network and can be considered as a kind of recurrent neural network with constrained weights.Then,given this and considering that a recurrent neural network has the recognition capability for target motion patterns,a new filter is developed in a unified neural network architecture and specifically constructed using feedforward neural network,recurrent neural network,and attention mechanism.And the unified tracking filter proposed in this paper can generate three aspects of unity:a unified target motion model,an adaptive filter method,and an overall track filtering framework.Finally,Simulation results show that the proposed filter is effective and useful,of which the overall performance is superior to those of compared filters.
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funded by the project“Design of System Integration Construction Scheme Based on Functions of Each Module” (No.XDHT2020169A)the project“Development of Indoor Inspection Robot System for Substation” (No.XDHT2019501A).
文摘In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.
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 National Hi-tech Research and Development Program of China (863 Program,Grant No.2008AA042408)
文摘Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method is confined to the expensive hardware equipments.This paper starts from Gabor transform and applies the Gabor time-frequency filtering to vibration signal.The order component＇s time-frequency coefficients are extracted by mask operation.The order component is reconstructed from the obtained coefficients.The following four key technologies,such as smoothing rotary speed curve,defining filtering band width,constructing the mask operation matrix and reconstructing signal component,are also deeply discussed.Moreover,the technique to smooth the rotary speed curve based on polynomial approximation,the method to determine filtering band width,the arithmetic to constitute mask array and the iterative algorithm to reconstruct signal based on minimum mean square error are specifically analyzed.The 4th order component is successfully gained by using the methods that Gabor time-frequency filter,and the validity and feasibility of this method are approved.This method can solve the problem of order tracking filter technologies which used to depend on hardware and efficiently improve the accuracy of order analysis.
基金supported by Program for New Century Excellent Talents in University of China (No.NCET-120030)National Natural Science Foundation of China (No.91438116)
文摘Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio（SNR） is unknown.If the frequency-locked loop（FLL） or the phase-locked loop（PLL） with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused.Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence.Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio.The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process.In addition, the upper diagonal（UD） factorization and innovation adaptive control are used to reduce model estimation errors,suppress filter divergence and improve filtering accuracy.The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect.
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.
基金the National Natural Science Foundation of China (60572038).
文摘When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.
基金Project supported by National High-Technology Research and De-velopment Program(Grant No .863 -2001AA422420-02)
文摘As a new method for dealing with any nonlinear or non-Ganssian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters （PF） are favored by researchers and widely applied in many fields. Based on particle filtering, an improved extended Kalman filter （EKF） proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for target tracking. Performances of the EKF, basic PF and the improved PF are compared in target tracking examples. The simulation results confirm that the improved particle filter outperforms the others.
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.
基金supported by the National Natural Science Foundation of China (No.60971104)the Program for New Century Excellent Talents inUniversity of China (No.NCET-05-0794)the Young Teacher Scientific Research Foundation of Southwest Jiaotong University (No.2009Q032)
文摘A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation, eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses, in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions. The last experimental results show that the proposed method not only improves the robustness for nonlinear eye tracking, but also can provide more accurate estimation than the traditional Kalman filter.
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This project was supported by the National Natural Science Foundation of China (50405017) .
文摘For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter （EKF） proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo （MCMC） method, the resampling step, ere novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.
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.
文摘A new improved particle filter algorithm with the simplified UT （unscented transformation） and the modified unscented Kalman filter （UKF） proposal distribution is presented. The scaling factor is added to adaptively estimate on line and to improve the filtering performance. An adaptive algorithm is developed. In the bearings-only tracking experiments, the results confirm the improved particle filter algorithm outperforms others.
基金sponsored by the Special Fund for Meteorological Research in the Public Interest from the Ministry of Science and Technology of China(Grant No.GYHY201306004)the National Key Basic Research Program of China(Grant No.2013CB430104)+1 种基金the National Natural Science Foundation of China(Grant Nos.41461164006,41375048 and 41425018)supported by the Ministry of Science and Technology of Taiwan(Grant No.MOST103-2111-M-002-011-MY3)
文摘This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot （2009） using a WRF-based ensemble Kalman filter （EnKF） data assimilation （DA） system. The results showed that the performance of radar EnKF DA was quite sensitive to the number of radars being assimilated and the DA timing relative to the landfall of the tropical cyclone （TC）. It was found that assimilating radial velocity （Vr） data from all the four operational radars during the 6 h immediately before TC landfall was quite important for the track and rainfall forecasts after the TC made landfall. The TC track forecast error could be decreased by about 43% and the 24-h rainfall forecast skill could be almost tripled. Assimilating Vr data from a single radar outperformed the experiment without DA, though with less improvement compared to the multiple-radar DA experiment. Different forecast performances were obtained by assimilating different radars, which was closely related to the first-time wind analysis increment, the location of moisture transport, the quasi-stationary rainband, and the local convergence line. However, only assimilating Vr data when the TC was farther away from making landfall might worsen TC track and rainfall forecasts. Besides, this work also demonstrated that Vr data from multiple radars, instead of a single radar, should be used for verification to obtain a more reliable assessment of the EnKF performance.
基金National Natural Science Foundations of China(Nos.51175082,60874092,51375088)
文摘To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is introduced and a fuzzy adaptive strong tracking cubature Kalman filter( FASTCKF) based on fuzzy logic controller is proposed. This method monitors residual absolute mean and standard deviation of each measurement component with fuzzy logic adaptive controller( FLAC),and adjusts the softening factor matrix dynamically by fuzzy rules,which is capable to modify suboptimal fading factor of STF adaptively and improve the filter's robust adaptive capacity. The simulation results show that the improved filtering performance is superior to the conventional square root cubature Kalman filter( SCKF) and the strong tracking square root cubature Kalman filter( STSCKF).
