ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal ...ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal P-QRS-T complexes of original signal is averaged and repeated and taken as a reference signal. LMS and RL5 adaptive filters algorithms are applied. The results showed that the fetal ECGs have been successfully detected. The accuracy of Daisy database was up to 84% of LM5 and 88% of RLS while PhysioNet was up to 98% and 96% for LMS and RLS respectively.展开更多
Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling t...Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.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
In-loop filtering significantly helps detect and remove blocking artifacts across block boundaries in low bitrate coded High Efficiency Video Coding(HEVC)frames and improves its subjective visual quality in multimedia...In-loop filtering significantly helps detect and remove blocking artifacts across block boundaries in low bitrate coded High Efficiency Video Coding(HEVC)frames and improves its subjective visual quality in multimedia services over communication networks.However,on faster processing of the complex videos at a low bitrate,some visible artifacts considerably degrade the picture quality.In this paper,we proposed a four-step fuzzy based adaptive deblocking filter selection technique.The proposed method removes the quantization noise,blocking artifacts and corner outliers efficiently for HEVC coded videos even at low bit-rate.We have considered Y(luma),U(chromablue),and V(chroma-red)components parallelly.Finally,we have developed a fuzzy system to detect blocking artifacts and use adaptive filters as per requirement in all four quadrants,namely up 45◦,down 45◦,up 135◦,and down 135◦across horizontal and vertical block boundaries.In this context,experimentation is done on a wide variety of videos.An objective and subjective analysis is carried out with MATLAB software and Human Visual System(HVS).The proposed method substantially outperforms existing postprocessing deblocking techniques in terms of YPSNR and BD_rate.In the proposed method,we achieved 0.32–0.97 dB values of YPSNR.Our method achieved a BD_rate of+1.69%for the luma component,−0.18%(U)and−1.99%(V)for chroma components,respectively,with respect to the stateof-the-art methods.The proposed method proves to have low computational complexity and has better parallel processing,hence suitable for a real-time system in the near future.展开更多
We first introduce a new approach for optimising a cascaded spline adaptive filter(CSAF)to identify unknown nonlinear systems by using a meta-heuristic optimisation algorithm(MOA).The CSAF architecture combines Hammer...We first introduce a new approach for optimising a cascaded spline adaptive filter(CSAF)to identify unknown nonlinear systems by using a meta-heuristic optimisation algorithm(MOA).The CSAF architecture combines Hammerstein and Wiener systems,where the nonlinear blocks are implemented with the spline network.The algorithms used optimise the weights of the spline interpolation function and linear filter by using an adequately weighted cost function,leading to improved filter stability,steady state performance,and guaranteed convergence to globally optimal solutions.We investigate two CSAF architectures:Hammerstein–Wiener SAF(HW-SAF)and Wiener–Hammerstein SAF(WH-SAF)structures.These architectures have been designed using gradient-based approaches which are inefficient due to poor convergence speed,and produce suboptimal solutions in a Gaussian noise environment.To avert these difficulties,we estimate the design parameters of the CSAF architecture using four independent MOAs:differential evolution(DE),brainstorm optimisation(BSO),multi-verse optimiser(MVO),and a recently proposed remora optimisation algorithm(ROA).In ROA,the remora factor’s control parameters produce near-global optimal parameters with a higher convergence speed.ROA also ensures the most balanced exploration and exploitation phases compared to DE-,BSO-,and MVO-based design approaches.Finally,the identification results of three numerical and industryspecific benchmark systems,including coupled electric drives,a thermic wall,and a continuous stirred tank reactor,are presented to emphasise the effectiveness of the ROA-based CSAF design.展开更多
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
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
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
In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabilities.In the early stage of video coding,in-loop filters,such as the d...In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabilities.In the early stage of video coding,in-loop filters,such as the deblocking filter,sample adaptive offset,and adaptive loop filter,were performed separately for each component.Recently,cross-component filters have been studied to improve chroma fidelity by exploiting correlations between the luma and chroma channels.This paper introduces the cross-component filters used in the state-ofthe-art video coding standards,including the cross-component adaptive loop filter and cross-component sample adaptive offset.Crosscomponent filters aim to reduce compression artifacts based on the correlation between different components and provide more accurate pixel reconstruction values.We present their origin,development,and status in the current video coding standards.Finally,we conduct discussions on the further evolution of cross-component filters.展开更多
Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive met...Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.展开更多
Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers...Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance.展开更多
The conventional feedforward hybrid active noise control(FFHANC)system combines the advantages of the feedforward narrowband active noise control(FFNANC)system and the feedforward broadband active noise control(FFBANC...The conventional feedforward hybrid active noise control(FFHANC)system combines the advantages of the feedforward narrowband active noise control(FFNANC)system and the feedforward broadband active noise control(FFBANC)system.To enhance its adaptive adjustment capability under frequency mismatch(FM)conditions,this paper introduces a narrowband frequency adaptive estimation module into the conventional FFHANC system.This module integrates an autoregressive(AR)model and a linear cascaded adaptive notch filter(LCANF),enabling accurate reference signal frequency estimation even under significant FM.Furthermore,in order to improve the coherence between narrowband and broadband components in the system’s error signal and its corresponding control filter for the conventional FFHANC system,this paper proposes an algorithm based on autoregressive bandpass filter bank(AR-BPFB)for error separation.Simulation results demonstrate that the proposed FFHANC system maintains robust performance under high FM conditions and effectively suppresses hybrid-band noise.The AR-BPFB algorithm significantly elevates the convergence speed of the FFHANC system.展开更多
This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable...This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable to nonlinear systems with Gaussian white noise.The system is reformulated with intermediate variables to expand the application of nonlinear systems under unknown inputs and non-Gaussian noises,which help decompose unknown input estimation into residual tracking and state observation subproblems.By introducing the orthogonal principle of innovation and attenuation factor,the intermediate variables-based filter can improve the estimation performance under non-Gaussian noises and unknown inputs.Simulation results validate the effectiveness of the proposed method.展开更多
Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b ...Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b service is experimentally analyzed first.Then,the ionosphere-free model is established.In order to solve the problem of slow convergence for traditional PPP,an adaptive robust extend Kalman filter(AREKF)algorithm is developed.Unlike the error compensation models,it reflects the noise information in real time by adjusting the covariance matrix of the measurements and the weight matrix of the state vector.The experimental results are analyzed last.Evaluation results indicate that the corrections provided by PPP-B2b can significantly reduce the discontinuous error of the orbits and clock offsets caused by broadcast ephemeris updating.Positioning results confirm that AREKF outperforms EKF both in static and kinematic modes.Around 20%improvement in accuracy and 25%improvement in convergence speed are achieved,making it valuable for PPP processing.展开更多
On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in t...On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.展开更多
The features of carrier-based aircraft’s navigation systems during the approach and landing phases are investigated.A new adaptive Kalman filter with unknown state noise statistics is proposed to improve the accuracy...The features of carrier-based aircraft’s navigation systems during the approach and landing phases are investigated.A new adaptive Kalman filter with unknown state noise statistics is proposed to improve the accuracy of the INS/GNSS integrated navigation system.The adaptive filtering algorithm aims to estimate and adapt the unknown state noise covariance Q in high dynamic conditions,when the measurement noise covariance R is assumed to be known empirically in advance.The new adaptive Kalman filter based on the innovation sequence and pseudo-measurement vector approach makes it more effective to estimate and adapt Q.The simulation results and semi-physical experiments show that the application of the proposed adaptive Kalman filter can guarantee a higher estimation accuracy of the state variables.展开更多
In order to measure the parameters of flight rocket by using radar,rocket impact point was estimated accurately for rocket trajectory correction.The Kalman filter with adaptive filter gain matrix was adopted.According...In order to measure the parameters of flight rocket by using radar,rocket impact point was estimated accurately for rocket trajectory correction.The Kalman filter with adaptive filter gain matrix was adopted.According to the particle trajectory model,the adaptive Kalman filter trajectory model was constructed for removing and filtering the outliers of the parameters during a section of flight detected by three-dimensional data radar and the rocket impact point was extrapolated.The results of numerical simulation show that the outliers and noise in trajectory measurement signal can be removed effectively by using the adaptive Kalman filter and the filter variance can converge in a short period of time.Based on the relation of filtering time and impact point estimation error,choosing the filtering time of 8-10 scan get the minimum estimation error of impact point.展开更多
A novel performance enhancement method of nonlinear sensor based on the Volterra series model is proposed. The Volterra series model, which is considered a nonlinear filter that can reduce sensor noise, presents an ef...A novel performance enhancement method of nonlinear sensor based on the Volterra series model is proposed. The Volterra series model, which is considered a nonlinear filter that can reduce sensor noise, presents an effective way for modeling and compensating a nonlinear sensor. In the experiment, the low accuracy pressure sensor MPX10 was used as the actual object, and higher accuracy sensor MPX2010 was used as the reference to provide the necessary teaching data for training the Volterra model. The simulation shows that the accuracy of MPX10 changes from 0.354-0.420 to 0.041-0.053 after the Volterra filter is applied. Obviously this scheme can effectively improve the sensor performance. Moreover, the scheme provides greater accuracy and environmental suitability for a nonlinear sensor.展开更多
This paper presents the principle and critical factors of adaptive cancellation of structural vibration in time domain(ACSV-TD).Digital-analog simulations and model tests are conducted on cancelling forced vibration o...This paper presents the principle and critical factors of adaptive cancellation of structural vibration in time domain(ACSV-TD).Digital-analog simulations and model tests are conducted on cancelling forced vibration of a cantilever beam.Filtered-X RLS algorithm is used to get faster convergence speed and stronger adaptability (in comparison with LMS algorithm). The results demonstrate the efficiency and adaptability of the ACSV-TD.展开更多
A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s LMS algorithm. It is based on the fact that LMS algorithm has properties of time delaying and low pass ...A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s LMS algorithm. It is based on the fact that LMS algorithm has properties of time delaying and low pass filtering. This paper shows that the algorithm, on the domain of {Ω 1:α∈(0,1)}×{Ω 2:β(0,∞)} , unbiasedly and asymptotically converges to the Winner solution when the signal is a stationary Gauss stochastic process. The convergent property and the performance misadjustment are analyzed in theory. And calculation method of the algorithm is also suggested. Numerical results given by computer simulations show that the algorithm is effective.展开更多
文摘ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal P-QRS-T complexes of original signal is averaged and repeated and taken as a reference signal. LMS and RL5 adaptive filters algorithms are applied. The results showed that the fetal ECGs have been successfully detected. The accuracy of Daisy database was up to 84% of LM5 and 88% of RLS while PhysioNet was up to 98% and 96% for LMS and RLS respectively.
基金Supported by the National Natural Science Foundation of China （No.40174009, No.40274002）.
文摘Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.
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In-loop filtering significantly helps detect and remove blocking artifacts across block boundaries in low bitrate coded High Efficiency Video Coding(HEVC)frames and improves its subjective visual quality in multimedia services over communication networks.However,on faster processing of the complex videos at a low bitrate,some visible artifacts considerably degrade the picture quality.In this paper,we proposed a four-step fuzzy based adaptive deblocking filter selection technique.The proposed method removes the quantization noise,blocking artifacts and corner outliers efficiently for HEVC coded videos even at low bit-rate.We have considered Y(luma),U(chromablue),and V(chroma-red)components parallelly.Finally,we have developed a fuzzy system to detect blocking artifacts and use adaptive filters as per requirement in all four quadrants,namely up 45◦,down 45◦,up 135◦,and down 135◦across horizontal and vertical block boundaries.In this context,experimentation is done on a wide variety of videos.An objective and subjective analysis is carried out with MATLAB software and Human Visual System(HVS).The proposed method substantially outperforms existing postprocessing deblocking techniques in terms of YPSNR and BD_rate.In the proposed method,we achieved 0.32–0.97 dB values of YPSNR.Our method achieved a BD_rate of+1.69%for the luma component,−0.18%(U)and−1.99%(V)for chroma components,respectively,with respect to the stateof-the-art methods.The proposed method proves to have low computational complexity and has better parallel processing,hence suitable for a real-time system in the near future.
文摘We first introduce a new approach for optimising a cascaded spline adaptive filter(CSAF)to identify unknown nonlinear systems by using a meta-heuristic optimisation algorithm(MOA).The CSAF architecture combines Hammerstein and Wiener systems,where the nonlinear blocks are implemented with the spline network.The algorithms used optimise the weights of the spline interpolation function and linear filter by using an adequately weighted cost function,leading to improved filter stability,steady state performance,and guaranteed convergence to globally optimal solutions.We investigate two CSAF architectures:Hammerstein–Wiener SAF(HW-SAF)and Wiener–Hammerstein SAF(WH-SAF)structures.These architectures have been designed using gradient-based approaches which are inefficient due to poor convergence speed,and produce suboptimal solutions in a Gaussian noise environment.To avert these difficulties,we estimate the design parameters of the CSAF architecture using four independent MOAs:differential evolution(DE),brainstorm optimisation(BSO),multi-verse optimiser(MVO),and a recently proposed remora optimisation algorithm(ROA).In ROA,the remora factor’s control parameters produce near-global optimal parameters with a higher convergence speed.ROA also ensures the most balanced exploration and exploitation phases compared to DE-,BSO-,and MVO-based design approaches.Finally,the identification results of three numerical and industryspecific benchmark systems,including coupled electric drives,a thermic wall,and a continuous stirred tank reactor,are presented to emphasise the effectiveness of the ROA-based CSAF design.
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.
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supported in part by National Science Foundation of China under Grant No.62031013PCL-CMCC Foundation for Science and Innovation under Grant No.2024ZY1C0040+1 种基金New Cornerstone Science Foundation for the Xplorer PrizeHigh performance Computing Platform of Peking University。
文摘In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabilities.In the early stage of video coding,in-loop filters,such as the deblocking filter,sample adaptive offset,and adaptive loop filter,were performed separately for each component.Recently,cross-component filters have been studied to improve chroma fidelity by exploiting correlations between the luma and chroma channels.This paper introduces the cross-component filters used in the state-ofthe-art video coding standards,including the cross-component adaptive loop filter and cross-component sample adaptive offset.Crosscomponent filters aim to reduce compression artifacts based on the correlation between different components and provide more accurate pixel reconstruction values.We present their origin,development,and status in the current video coding standards.Finally,we conduct discussions on the further evolution of cross-component filters.
文摘Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.
基金supported by the Foundation of Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology,Ministry of Education,Chinathe National Natural Science Foundation of China (61873064)
文摘Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance.
基金supported in part by the Postgraduate Research&Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(No.xcxjh20240326).
文摘The conventional feedforward hybrid active noise control(FFHANC)system combines the advantages of the feedforward narrowband active noise control(FFNANC)system and the feedforward broadband active noise control(FFBANC)system.To enhance its adaptive adjustment capability under frequency mismatch(FM)conditions,this paper introduces a narrowband frequency adaptive estimation module into the conventional FFHANC system.This module integrates an autoregressive(AR)model and a linear cascaded adaptive notch filter(LCANF),enabling accurate reference signal frequency estimation even under significant FM.Furthermore,in order to improve the coherence between narrowband and broadband components in the system’s error signal and its corresponding control filter for the conventional FFHANC system,this paper proposes an algorithm based on autoregressive bandpass filter bank(AR-BPFB)for error separation.Simulation results demonstrate that the proposed FFHANC system maintains robust performance under high FM conditions and effectively suppresses hybrid-band noise.The AR-BPFB algorithm significantly elevates the convergence speed of the FFHANC system.
基金Supported by the Foreign Experts Project of the Belt and Road Innovative Talent Exchange(No.DL2023016005L).
文摘This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable to nonlinear systems with Gaussian white noise.The system is reformulated with intermediate variables to expand the application of nonlinear systems under unknown inputs and non-Gaussian noises,which help decompose unknown input estimation into residual tracking and state observation subproblems.By introducing the orthogonal principle of innovation and attenuation factor,the intermediate variables-based filter can improve the estimation performance under non-Gaussian noises and unknown inputs.Simulation results validate the effectiveness of the proposed method.
文摘Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b service is experimentally analyzed first.Then,the ionosphere-free model is established.In order to solve the problem of slow convergence for traditional PPP,an adaptive robust extend Kalman filter(AREKF)algorithm is developed.Unlike the error compensation models,it reflects the noise information in real time by adjusting the covariance matrix of the measurements and the weight matrix of the state vector.The experimental results are analyzed last.Evaluation results indicate that the corrections provided by PPP-B2b can significantly reduce the discontinuous error of the orbits and clock offsets caused by broadcast ephemeris updating.Positioning results confirm that AREKF outperforms EKF both in static and kinematic modes.Around 20%improvement in accuracy and 25%improvement in convergence speed are achieved,making it valuable for PPP processing.
文摘On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.
基金supported by the project“Component’s digital transformation methods'fundamental research for micro-and nanosystems”(No.#0705-2020-0041).
文摘The features of carrier-based aircraft’s navigation systems during the approach and landing phases are investigated.A new adaptive Kalman filter with unknown state noise statistics is proposed to improve the accuracy of the INS/GNSS integrated navigation system.The adaptive filtering algorithm aims to estimate and adapt the unknown state noise covariance Q in high dynamic conditions,when the measurement noise covariance R is assumed to be known empirically in advance.The new adaptive Kalman filter based on the innovation sequence and pseudo-measurement vector approach makes it more effective to estimate and adapt Q.The simulation results and semi-physical experiments show that the application of the proposed adaptive Kalman filter can guarantee a higher estimation accuracy of the state variables.
文摘In order to measure the parameters of flight rocket by using radar,rocket impact point was estimated accurately for rocket trajectory correction.The Kalman filter with adaptive filter gain matrix was adopted.According to the particle trajectory model,the adaptive Kalman filter trajectory model was constructed for removing and filtering the outliers of the parameters during a section of flight detected by three-dimensional data radar and the rocket impact point was extrapolated.The results of numerical simulation show that the outliers and noise in trajectory measurement signal can be removed effectively by using the adaptive Kalman filter and the filter variance can converge in a short period of time.Based on the relation of filtering time and impact point estimation error,choosing the filtering time of 8-10 scan get the minimum estimation error of impact point.
基金TheNaturalScienceFoundationofGuangdong Province (No.0 3 2 0 3 0 ) .
文摘A novel performance enhancement method of nonlinear sensor based on the Volterra series model is proposed. The Volterra series model, which is considered a nonlinear filter that can reduce sensor noise, presents an effective way for modeling and compensating a nonlinear sensor. In the experiment, the low accuracy pressure sensor MPX10 was used as the actual object, and higher accuracy sensor MPX2010 was used as the reference to provide the necessary teaching data for training the Volterra model. The simulation shows that the accuracy of MPX10 changes from 0.354-0.420 to 0.041-0.053 after the Volterra filter is applied. Obviously this scheme can effectively improve the sensor performance. Moreover, the scheme provides greater accuracy and environmental suitability for a nonlinear sensor.
文摘This paper presents the principle and critical factors of adaptive cancellation of structural vibration in time domain(ACSV-TD).Digital-analog simulations and model tests are conducted on cancelling forced vibration of a cantilever beam.Filtered-X RLS algorithm is used to get faster convergence speed and stronger adaptability (in comparison with LMS algorithm). The results demonstrate the efficiency and adaptability of the ACSV-TD.
文摘A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s LMS algorithm. It is based on the fact that LMS algorithm has properties of time delaying and low pass filtering. This paper shows that the algorithm, on the domain of {Ω 1:α∈(0,1)}×{Ω 2:β(0,∞)} , unbiasedly and asymptotically converges to the Winner solution when the signal is a stationary Gauss stochastic process. The convergent property and the performance misadjustment are analyzed in theory. And calculation method of the algorithm is also suggested. Numerical results given by computer simulations show that the algorithm is effective.
