The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is in...The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference.展开更多
Based on the superiority of adaptive filtering algorithms designed with hyperbolic function-like objective functions,this paper proposes generalized spline adaptive filtering(SAF)algorithms designed with hyperbolic fu...Based on the superiority of adaptive filtering algorithms designed with hyperbolic function-like objective functions,this paper proposes generalized spline adaptive filtering(SAF)algorithms designed with hyperbolic function-like objective functions.Specifically,a series of generalized new SAF algorithms are proposed by introducing the q-deformed hyperbolic function as the cost function,named SAF-qDHSI,SAF-qDHCO,SAFqDHTA&SAF-qDHSE algorithms,respectively.Then,the proposed algorithm is theoretically demonstrated with detailed mean convergence and computational complexity analysis;secondly,the effect of different q values on the performance of the new algorithm is verified through data simulation;the new algorithm still has better performance under the interference of Gaussian noise and non-Gaussian noise even when facing the system mutation;finally,the new algorithm is verified through the measured engineering data,and the results show that the new algorithm has better convergence and robustness compared with the existing algorithm.In conclusion,the generalized algorithm based on the new cost function proposed in this paper is more effective in nonlinear system identification.展开更多
Global Navigation Satellite Systems(GNSSs)face significant security threats from spoofing attacks.Typical anti-spoofing methods rely on estimating the delays between spoofing and authentic signals using multicorrelato...Global Navigation Satellite Systems(GNSSs)face significant security threats from spoofing attacks.Typical anti-spoofing methods rely on estimating the delays between spoofing and authentic signals using multicorrelator outputs.However,the accuracy of the delay estimation is limited by the spacing of the correlators.To address this,an innovative anti-spoofing method is introduced,which incorporates distinct coarse and refined stages for more accurate spoofing estimation.By leveraging the coarse delay estimates obtained through maximum likelihood estimation,the proposed method establishes the Windowed Sum of the Relative Delay(WSRD)statistics to detect the presence of spoofing signals.The iterative strategy is then employed to enhance the precision of the delay estimation.To further adapt to variations in the observation noise caused by spoofing intrusions and restore precise position,velocity,and timing solutions,an adaptive extended Kalman filter is proposed.This comprehensive framework offers detection,mitigation,and recovery against spoofing attacks.Experimental validation using datasets from the Texas Spoofing Test Battery(TEXBAT)demonstrates the effectiveness of the proposed anti-spoofing method.With 41 correlators,the method achieves a detection rate exceeding 90%at a false alarm rate of 10-5,with position or time errors below 15 m.Notably,this refined anti-spoofing approach shows robust detection and mitigation capabilities,requiring only a single antenna without the need for additional external sensors.These advancements can significantly contribute to the development of GNSS anti-spoofing measures.展开更多
Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.I...Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.展开更多
In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criter...In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criterion.Afterwards, reciprocal of the antenna pattern is defined as the spatial spectrum and the extracted peak values are corresponded to the estimated DOA. Through observation of the spectrum and data analysis of variable steps and SNRs, the simulation results demonstrate that the proposed method can estimate DOA above board. Furthermore, the estimation error of the proposed technique is directly proportional to step size and is inversely proportional to SNR. Unlike the existing MUSIC algorithm, the proposed algorithm has less computational complexity as it eliminates the need of estimating the number of signals and the eigenvalue decomposition of covariance matrix. Also it outperforms MUSIC algorithm, the recently proposed MUSIC-Like algorithm and classical methods by achieving better resolution with narrow width of peaks.展开更多
The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorit...The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.展开更多
In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the ...In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.展开更多
In kinematic navigation and positioning,abnormal observations and kinematic model disturbances are one of the key factors affecting the stability and reliability of positioning performance.Generally,robust adaptive fi...In kinematic navigation and positioning,abnormal observations and kinematic model disturbances are one of the key factors affecting the stability and reliability of positioning performance.Generally,robust adaptive filtering algorithm is used to reduce the influence of them on positioning results.However,it is difficult to accurately identify and separate the influence of abnormal observations and kinematic model disturbances on positioning results,especially in the application of kinematic Precise Point Positioning(PPP).This has always been a key factor limiting the performance of conventional robust adaptive filtering algorithms.To address this problem,this paper proposes a two-step robust adaptive filtering algorithm,which includes two filtering steps:without considering the kinematic model information,the first step of filtering only detects the abnormal observations.Based on the filtering results of the first step,the second step makes further detection on the kinematic model disturbances and conducts adaptive processing.Theoretical analysis and experiment results indicate that the two-step robust adaptive filtering algorithm can further enhance the robustness of the filtering against the influence of abnormal observations and kinematic model disturbances on the positioning results.Ultimately,improvement of the stability and reliability of kinematic PPP is significant.展开更多
An implementation of adaptive filtering,composed of an unsupervised adaptive filter(UAF),a multi-step forward linear predictor(FLP),and an unsupervised multi-step adaptive predictor(UMAP),is built for suppressing impu...An implementation of adaptive filtering,composed of an unsupervised adaptive filter(UAF),a multi-step forward linear predictor(FLP),and an unsupervised multi-step adaptive predictor(UMAP),is built for suppressing impulsive noise in unknown circumstances.This filtering scheme,called unsupervised robust adaptive filter(URAF),possesses a switching structure,which ensures the robustness against impulsive noise.The FLP is used to detect the possible impulsive noise added to the signal,if the signal is"impulse-free",the filter UAF can estimate the clean sig-nal.If there exists impulsive noise,the impulse corrupted samples are replaced by predicted ones from the FLP,and then the UMAP estimates the clean signal.Both the simulation and experimental results show that the URAF has a better rate of convergence than the most recent universal filter,and is effective to restrict large disturbance like impulsive noise when the universal filter fails.展开更多
Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise ar...Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise are unknown. This filtering algorithm is a simple recursive structure. A simulation example is presented which demonstrates the effectiveness of this filtering algorithm.展开更多
With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has establishe...With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.展开更多
Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation...Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.展开更多
An unsupervised minimum mean square error FIR adaptive filtering （UAF） algorithm is proposed to estimate the system＇ s input signal. The algorithm only uses the system＇ s output signal and noise variance without r...An unsupervised minimum mean square error FIR adaptive filtering （UAF） algorithm is proposed to estimate the system＇ s input signal. The algorithm only uses the system＇ s output signal and noise variance without requiring knowledge of a reference signal. The frequency analysis shows that the UAF is a multi-spot bandpass filter with passing frequency determined by the system＇ s input signal. Namely, the UAF chooses the expected frequency and extremely restricts the unwanted fre- quency signal by using weight-updating scheme in time domain. However, the UAF presents the Gibbs phenomenon since the ideal filter is infinitely long which is unrealizable. The simulation and experimental results show that the UAF could effectively reduce the amplitude of the noise and im- prove the signal to noise ratio.展开更多
The negative impact on communication performance in wireless multi-hop communication net-work caused by limited bandwidth,high bit eror rate （BER）,fading,noise and interference is alleviated by an adaptive filtering...The negative impact on communication performance in wireless multi-hop communication net-work caused by limited bandwidth,high bit eror rate （BER）,fading,noise and interference is alleviated by an adaptive filtering game based on frequency subbands selection and predetemined threshold.Such threshold is being obtained in Gaussian and multipath fading channel according to the frequency-matching principle and BER performance.The dynamic selection of subbands will obtain high use efficiency without the help of frequency hopping,and propound a new thought to improve band limited communication for wireless multi-hop communication network.The effectiveness of the adaptive filtering method has been verified by interleaving spread spectrum orthogonal frequency division multiplexing （ISS-OFDM） in different interference conditions,and the simulating results based on network simulator 2 （NS2） indicate that system BER can be improved greatly.展开更多
On the basis of studying the standard TCP retransmission mechanism, this paper proposes a method to adopt the theory of adaptive filtering in the field for the estimation of round trip time （RTT）. Then this method i...On the basis of studying the standard TCP retransmission mechanism, this paper proposes a method to adopt the theory of adaptive filtering in the field for the estimation of round trip time （RTT）. Then this method is embedded in TCP for estimation of RTT, and defined as RTT-AF TCP. In the schemes with Only-TCP flow and with TCP and UDP flow fighting for the link, the experiment results were made and analyzed by using the standard TCP and the RTT-AF TCP. The experimental results show higher throughput and lower loss rate of packets by using RTT-AF TCP for data transmission, and achieve excellent effect.展开更多
Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performan...Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.展开更多
The objective of this work is to improve the measurement accuracy of a gyroscope on a angular motion base with a simple adaptive filter scheme.Two main topics are highlighted in this work.The first topic is to show bu...The objective of this work is to improve the measurement accuracy of a gyroscope on a angular motion base with a simple adaptive filter scheme.Two main topics are highlighted in this work.The first topic is to show building a dual-process model employed for the conventional Kalman filter.The second topic is to show developing a modified noise adaptive algorithm when measurement noise and process noise are unknown.The experimental results are presented to show that the simple adaptive filtering scheme outperforms the other conventional scheme in this paper in terms of noise reduction.展开更多
By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based ...By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based on different parameters.Compared with the existing algorithms through the simulation,the proposed algorithm has faster convergence speed and smaller steady state error.展开更多
Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when ...Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.展开更多
Vehicle tracking plays a crucial role in intelligent transportation, autonomous driving, and video surveillance. However, challenges such as occlusion, multi-target interference, and nonlinear motion in dynamic scenar...Vehicle tracking plays a crucial role in intelligent transportation, autonomous driving, and video surveillance. However, challenges such as occlusion, multi-target interference, and nonlinear motion in dynamic scenarios make tracking accuracy and stability a focus of ongoing research. This paper proposes an integrated method combining YOLOv8 object detection with adaptive Kalman filtering. The approach employs a support vector machine (SVM) to dynamically select the optimal filter (including standard Kalman filter, extended Kalman filter, and unscented Kalman filter), enhancing the system’s adaptability to different motion patterns. Additionally, an error feedback mechanism is incorporated to dynamically adjust filter parameters, further improving responsiveness to sudden events. Experimental results on the KITTI and UA-DETRAC datasets demonstrate that the proposed method significantly improves detection accuracy (mAP@0.5 increased by approximately 3%), tracking accuracy (MOTA improved by 5%), and system robustness, providing an efficient solution for vehicle tracking in complex environments.展开更多
基金supported by the Shan⁃dong Provincial Natural Science Foundation(No.ZR2022MF314).
文摘The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference.
基金financially supported by the National Natural Science Foundation of China (8225041038)the Sichuan Science and Technology Program (23NSFSC2916)the Fundamental Research Funds for the Central Universities, Southwest Minzu University (ZYN2024077)
文摘Based on the superiority of adaptive filtering algorithms designed with hyperbolic function-like objective functions,this paper proposes generalized spline adaptive filtering(SAF)algorithms designed with hyperbolic function-like objective functions.Specifically,a series of generalized new SAF algorithms are proposed by introducing the q-deformed hyperbolic function as the cost function,named SAF-qDHSI,SAF-qDHCO,SAFqDHTA&SAF-qDHSE algorithms,respectively.Then,the proposed algorithm is theoretically demonstrated with detailed mean convergence and computational complexity analysis;secondly,the effect of different q values on the performance of the new algorithm is verified through data simulation;the new algorithm still has better performance under the interference of Gaussian noise and non-Gaussian noise even when facing the system mutation;finally,the new algorithm is verified through the measured engineering data,and the results show that the new algorithm has better convergence and robustness compared with the existing algorithm.In conclusion,the generalized algorithm based on the new cost function proposed in this paper is more effective in nonlinear system identification.
基金co-supported by the Tianjin Research innovation Project for Postgraduate Students,China(No.2022BKYZ039)the China Postdoctoral Science Foundation(No.2023M731788)the National Natural Science Foundation of China(No.62303246)。
文摘Global Navigation Satellite Systems(GNSSs)face significant security threats from spoofing attacks.Typical anti-spoofing methods rely on estimating the delays between spoofing and authentic signals using multicorrelator outputs.However,the accuracy of the delay estimation is limited by the spacing of the correlators.To address this,an innovative anti-spoofing method is introduced,which incorporates distinct coarse and refined stages for more accurate spoofing estimation.By leveraging the coarse delay estimates obtained through maximum likelihood estimation,the proposed method establishes the Windowed Sum of the Relative Delay(WSRD)statistics to detect the presence of spoofing signals.The iterative strategy is then employed to enhance the precision of the delay estimation.To further adapt to variations in the observation noise caused by spoofing intrusions and restore precise position,velocity,and timing solutions,an adaptive extended Kalman filter is proposed.This comprehensive framework offers detection,mitigation,and recovery against spoofing attacks.Experimental validation using datasets from the Texas Spoofing Test Battery(TEXBAT)demonstrates the effectiveness of the proposed anti-spoofing method.With 41 correlators,the method achieves a detection rate exceeding 90%at a false alarm rate of 10-5,with position or time errors below 15 m.Notably,this refined anti-spoofing approach shows robust detection and mitigation capabilities,requiring only a single antenna without the need for additional external sensors.These advancements can significantly contribute to the development of GNSS anti-spoofing measures.
文摘Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.
基金support of the Science and Technology Commission of Chongqing through the Nature Science Fund (2013jj B40005)supported by the Fundamental Research Funds for the Central University (106112016CDJZR165508) of China
文摘In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criterion.Afterwards, reciprocal of the antenna pattern is defined as the spatial spectrum and the extracted peak values are corresponded to the estimated DOA. Through observation of the spectrum and data analysis of variable steps and SNRs, the simulation results demonstrate that the proposed method can estimate DOA above board. Furthermore, the estimation error of the proposed technique is directly proportional to step size and is inversely proportional to SNR. Unlike the existing MUSIC algorithm, the proposed algorithm has less computational complexity as it eliminates the need of estimating the number of signals and the eigenvalue decomposition of covariance matrix. Also it outperforms MUSIC algorithm, the recently proposed MUSIC-Like algorithm and classical methods by achieving better resolution with narrow width of peaks.
基金Project(50905037) supported by the National Natural Science Foundation of ChinaProject(20092304120014) supported by Specialized Research Fund for the Doctoral Program of Higher Education of China+2 种基金 Project(20100471021) supported by the China Postdoctoral Science Foundation Project(LBH-Q09134) supported by Heilongjiang Postdoctoral Science-Research Foundation,China Project (HEUFT09013) supported by the Foundation of Harbin Engineering University,China
文摘The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.
基金The Key Program of National Natural Science of China(No.U1261205)Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.
基金co-supported by the National Natural Science Foundation of China(No.41874034)the National key research and development program of China(No.2016YFB0502102)+1 种基金the Beijing Natural Science Foundation(No.4202041)the Aeronautical Science Foundation of China(No.2016ZC51024)。
文摘In kinematic navigation and positioning,abnormal observations and kinematic model disturbances are one of the key factors affecting the stability and reliability of positioning performance.Generally,robust adaptive filtering algorithm is used to reduce the influence of them on positioning results.However,it is difficult to accurately identify and separate the influence of abnormal observations and kinematic model disturbances on positioning results,especially in the application of kinematic Precise Point Positioning(PPP).This has always been a key factor limiting the performance of conventional robust adaptive filtering algorithms.To address this problem,this paper proposes a two-step robust adaptive filtering algorithm,which includes two filtering steps:without considering the kinematic model information,the first step of filtering only detects the abnormal observations.Based on the filtering results of the first step,the second step makes further detection on the kinematic model disturbances and conducts adaptive processing.Theoretical analysis and experiment results indicate that the two-step robust adaptive filtering algorithm can further enhance the robustness of the filtering against the influence of abnormal observations and kinematic model disturbances on the positioning results.Ultimately,improvement of the stability and reliability of kinematic PPP is significant.
基金supported by the National Science Fund for Distinguished Young Scholars of China(60925011)
文摘An implementation of adaptive filtering,composed of an unsupervised adaptive filter(UAF),a multi-step forward linear predictor(FLP),and an unsupervised multi-step adaptive predictor(UMAP),is built for suppressing impulsive noise in unknown circumstances.This filtering scheme,called unsupervised robust adaptive filter(URAF),possesses a switching structure,which ensures the robustness against impulsive noise.The FLP is used to detect the possible impulsive noise added to the signal,if the signal is"impulse-free",the filter UAF can estimate the clean sig-nal.If there exists impulsive noise,the impulse corrupted samples are replaced by predicted ones from the FLP,and then the UMAP estimates the clean signal.Both the simulation and experimental results show that the URAF has a better rate of convergence than the most recent universal filter,and is effective to restrict large disturbance like impulsive noise when the universal filter fails.
文摘Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise are unknown. This filtering algorithm is a simple recursive structure. A simulation example is presented which demonstrates the effectiveness of this filtering algorithm.
文摘With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.
基金Supported by the National Natural Science Foundation of China,no.69672039
文摘Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.
文摘An unsupervised minimum mean square error FIR adaptive filtering （UAF） algorithm is proposed to estimate the system＇ s input signal. The algorithm only uses the system＇ s output signal and noise variance without requiring knowledge of a reference signal. The frequency analysis shows that the UAF is a multi-spot bandpass filter with passing frequency determined by the system＇ s input signal. Namely, the UAF chooses the expected frequency and extremely restricts the unwanted fre- quency signal by using weight-updating scheme in time domain. However, the UAF presents the Gibbs phenomenon since the ideal filter is infinitely long which is unrealizable. The simulation and experimental results show that the UAF could effectively reduce the amplitude of the noise and im- prove the signal to noise ratio.
基金Supported by the National Nature Science Foundation of China(No.61302074)the Specialized Research Fund for the Doctoral Program of Higher Education(No.20122301120004)+1 种基金the Natural Science Foundation of Heilongjiang Province(No.QC2013C061)Research Foundation of Education Bureau of Heilongjiang Province(No.12531480)
文摘The negative impact on communication performance in wireless multi-hop communication net-work caused by limited bandwidth,high bit eror rate （BER）,fading,noise and interference is alleviated by an adaptive filtering game based on frequency subbands selection and predetemined threshold.Such threshold is being obtained in Gaussian and multipath fading channel according to the frequency-matching principle and BER performance.The dynamic selection of subbands will obtain high use efficiency without the help of frequency hopping,and propound a new thought to improve band limited communication for wireless multi-hop communication network.The effectiveness of the adaptive filtering method has been verified by interleaving spread spectrum orthogonal frequency division multiplexing （ISS-OFDM） in different interference conditions,and the simulating results based on network simulator 2 （NS2） indicate that system BER can be improved greatly.
基金Supported by the National Natural Science Foundation of China （No. 60775058） the Key Project of Ministry of Education of China （No. 107028）.
文摘On the basis of studying the standard TCP retransmission mechanism, this paper proposes a method to adopt the theory of adaptive filtering in the field for the estimation of round trip time （RTT）. Then this method is embedded in TCP for estimation of RTT, and defined as RTT-AF TCP. In the schemes with Only-TCP flow and with TCP and UDP flow fighting for the link, the experiment results were made and analyzed by using the standard TCP and the RTT-AF TCP. The experimental results show higher throughput and lower loss rate of packets by using RTT-AF TCP for data transmission, and achieve excellent effect.
基金supported by the National Natural Science Foundation of China(61871420)the Natural Science Foundation of Sichuan Province,China(23NSFSC2916)the introduction of talent,Southwest MinZu University,China,funding research projects start(RQD2021064).
文摘Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.
文摘The objective of this work is to improve the measurement accuracy of a gyroscope on a angular motion base with a simple adaptive filter scheme.Two main topics are highlighted in this work.The first topic is to show building a dual-process model employed for the conventional Kalman filter.The second topic is to show developing a modified noise adaptive algorithm when measurement noise and process noise are unknown.The experimental results are presented to show that the simple adaptive filtering scheme outperforms the other conventional scheme in this paper in terms of noise reduction.
基金Natural Science Foundation of Shandong Province of China(No.ZR2012FM011)Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based on different parameters.Compared with the existing algorithms through the simulation,the proposed algorithm has faster convergence speed and smaller steady state error.
基金supported in part by the National Key Research and Development Program of China(2023YFB3906403)the National Natural Science Foundation of China(62373118,62173105)the Natural Science Foundation of Heilongjiang Province of China(ZD2023F002)
文摘Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.
文摘Vehicle tracking plays a crucial role in intelligent transportation, autonomous driving, and video surveillance. However, challenges such as occlusion, multi-target interference, and nonlinear motion in dynamic scenarios make tracking accuracy and stability a focus of ongoing research. This paper proposes an integrated method combining YOLOv8 object detection with adaptive Kalman filtering. The approach employs a support vector machine (SVM) to dynamically select the optimal filter (including standard Kalman filter, extended Kalman filter, and unscented Kalman filter), enhancing the system’s adaptability to different motion patterns. Additionally, an error feedback mechanism is incorporated to dynamically adjust filter parameters, further improving responsiveness to sudden events. Experimental results on the KITTI and UA-DETRAC datasets demonstrate that the proposed method significantly improves detection accuracy (mAP@0.5 increased by approximately 3%), tracking accuracy (MOTA improved by 5%), and system robustness, providing an efficient solution for vehicle tracking in complex environments.
