Adaptive Filter Research Articles

Cycle Fluctuations in the U.S. Real GDP and National Income and Product Accounts Aggregates

This article provides a new set of empirical regularities describing the U.S. macroeconomy, focusing on business cycle fluctuations in the real GDP and eleven major aggregates from the U.S. National Income and Product Accounts (NIPAs). Patterns of the cyclical fluctuations are assessed using filtering methodologies that adapt to the properties of the series being studied. We employ a recent dataset that includes the Great Recession caused by the 2008 financial crisis and the ensuing recovery. We aim to (1) examine the lead-lag relations via cross-correlations between the aggregate cycle in the U.S. real GDP and the cyclical movements in the eleven major NIPA aggregates; (2) investigate econometrically the inter-linkage between the cyclical component of real GDP and that of each NIPA aggregate by way of the Granger-causality test; and (3) evaluate the capability or the predictive power of each NIPA aggregate to forecast real GDP growth via three forecasting models. Comparisons are made with popular nonparametric HP and Baxter-King (BK) filters. The basic conclusion from the empirical analysis is that the adaptive model-based filters have demonstrated advantages over the commonly used HP and BK filters for business cycle analysis across very diverse economic series from the U.S. national accounts, and the structural time series model using smoothed trend and cycle estimates produced more accurate forecasts than the AR(p) model that forecasts directly using the unfiltered time series.

An adaptive spatiotemporal filter for ultrasound localization microscopy based on density canopy clustering

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.

Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

Design and implementation of an adaptive unscented Kalman filter with interval Type-3 fuzzy set for an attitude and heading reference system considering gyroscope bias

Low-cost microelectromechanical sensors used in attitude and heading reference systems (AHRS) suffer from unstable on–off biases, scaling errors, and nonlinearities that lead to an increase of navigation errors over time. To damp these errors and uncertainties from ultimate attitude and heading angles, the bias term of gyroscopes is augemented in under estimation state vector for high frequency feedback compensation. In this paper, an adaptive estimation algorithm to combine the strong tracking filter (STF) and interval type-3 fuzzy set (IT3FS) with an unscented Kalman filter (UKF) is proposed to deal with the large uncertainties in the microelectromechanical AHRS. The UKF, involving strong capability of dealing with nonlinearity, is limited by large instable bias uncertainty. By combining the STF to adaptively tuning of the UKF gain matrix and the IT3FS to determine the STF fading factor as well, the new IT3FS-STUKF algorithm is developed for high estimation performance against the significant uncertainties. To validate the proposed IT3FS-STUKF, real experiments are conducted with ground vehicles in urban areas, which show an improved accuracy compared to the extended Kalman filter (EKF) at reasonable computational burden.

Performance analysis of unconstrained partitioned-block frequency-domain adaptive filters in under-modeling scenarios

The unconstrained partitioned-block frequency-domain adaptive filter (PBFDAF) offers superior computational efficiency over its constrained counterpart. However, the correlation matrix governing the natural modes of the unconstrained PBFDAF is not full rank. Consequently, the mean coefficient behavior of the algorithm depends on the initialization of adaptive coefficients and the Wiener solution is non-unique. To address the above problems, a new theoretical model for the deficient-length unconstrained PBFDAF is proposed by constructing a modified filter weight vector within a system identification framework. Specifically, we analyze the transient and steady-state convergence behavior. Our analysis reveals that modified weight vector is independent of its initialization in the steady state. The deficient-length unconstrained PBFDAF converges to a unique Wiener solution, which does not match the true impulse response of the unknown plant. However, the unconstrained PBFDAF can recover more coefficients of the parameter vector of the unknown system than the constrained PBFDAF in certain cases. Also, the modified filter coefficient yields better mean square deviation (MSD) performance than previously assumed. The presented alternative performance analysis provides new insight into convergence properties of the deficient-length unconstrained PBFDAF. Simulations validate the analysis based on the proposed theoretical model.

Top oil temperature prediction at a multiple time scale for power transformers based on adaptive extended Kalman filter

Top oil temperature prediction at a multiple time scale for power transformers based on adaptive extended Kalman filter

A hybrid model for state of charge estimation of lithium-ion batteries utilizing improved adaptive extended Kalman filter and long short-term memory neural network

With lithium-ion batteries being utilized in all aspects of life, accurately estimating the state of charge (SOC) of a battery has become a key issue in battery management systems. In this paper, an improved hybrid model based on adaptive extended Kalman filter (AEKF) and improved long short-term memory (ILSTM) neural network is proposed. The proposed model is based on a second-order RC equivalent circuit model, and the dynamic forgetting factor recursive least squares (DFFRLS) and AEKF algorithms are utilized to obtain the initial SOC estimates. And the estimation error in the AEKF algorithm due to neglecting the higher order terms of the Taylor expansion equations is compensated by the improvement of the LSTM network. The results under different working conditions indicate that the SOC estimation of the hybrid model has good convergence and high system robustness. The maximum error (MAX) of this algorithm is less than 2.3 %, especially the root mean square error (RMSE) and mean absolute error (MAE) are less than 0.84 % and 0.65 %, respectively.

Dynamic Tracking Method Based on Improved DeepSORT for Electric Vehicle

The development of electric vehicles has facilitated intelligent transportation, which requires the swift and effective detection and tracking of moving vehicles. To satisfy this demand, this paper presents an enhanced DeepSORT algorithm. By selecting YOLO-SSFS as the front-end detector and incorporating a lightweight and high-precision feature training network called FasterNet, the proposed method effectively extracts vehicle appearance attributes. Besides this, the noise scale adaptive Kalman filter is implemented and the conventional cascade matching process is substituted with global join matching, thereby enhancing overall performance and tracking accuracy. Validation conducted on the VisDrone dataset demonstrates the superiority of this method compared to the original DeepSORT algorithm, exhibiting a 4.76% increase in tracking accuracy and a 3.10% improvement in tracking precision. The findings reveal the advantages of the algorithms in the domain of vehicle detection and tracking, allowing significant technological advancements in intelligent transportation systems.

Online numerical simulation method based on adaptive unscented Kalman filter for shear wall structures

Hybrid test method is an effective approach to evaluate the seismic performance of engineering structures. Particularly, online numerical simulation method (ONSM) based on unscented Kalman filter (UKF) model updating significantly improves the accuracy of the hybrid test results. However, the sensitivity of UKF to the initial values of the state vector, state covariance, and observation noise covariance may lead to the distortion of hybrid test results. To address these issues, one novel ONSM based on adaptive UKF (ONSM-AUKF) is proposed in this paper. The AUKF algorithm incorporates an adaptive variance module to maintain equilibrium. This not only reduces the sensitivity of UKF to the initial parameter settings, prevents filter divergence, but also enhances the accuracy and stability of estimation. The proposed ONSM-AUKF accurately identifies the constitutive model parameters by utilizing measured data from specimens through the AUKF algorithm, which improves the accuracy and the stability of parameter estimation. The simulation results indicate that AUKF algorithm has the capability to decrease the sensitivity of initial parameter settings, thus enhancing the stability and robustness of parameter estimation in comparison to the UKF algorithm. The experimental results validate the feasibility and effectiveness of the proposed ONSM-AUKF, as well as the advantages of the ONSM-AUKF over the ONSM-UKF. The results indicate that the proposed ONSM-AUKF may have broad application prospect in evaluating the seismic performance of various of complex engineering structures.

Research on Vehicle Stability Control Based on a Union Disturbance Observer and Improved Adaptive Unscented Kalman Filter

This paper considers external disturbances imposed on vehicle systems. Based on a vehicle dynamics model of the vehicle with three degrees of freedom (3-DOFs), a union disturbance observer (UDO) composed of a nonlinear disturbance observer (NDO) and an extended state observer (ESO) was designed to obtain external disturbances and unmodeled items. Meanwhile, an improved adaptive unscented Kalman filter (iAUKF) with anti-disturbance and anti-noise properties is proposed, based on the UDO and the unscented Kalman filter (UKF) method, to evaluate the sideslip angle of vehicle systems. Finally, a vehicle yaw stability controller was designed based on UDO and the global fast terminal sliding mode control (GFTSMC) method. The results of co-simulation demonstrated that the proposed UDO was effectively able to observe external disturbances and unmodeled items. The proposed iAUKF, which considers external disturbances, not only achieves adaptive updating and adjustment of filtering parameters under different sensor noise intensities but can also resist external disturbances, improving the estimation accuracy and robustness of the UKF. In the anti-disturbance performance test, the maximum estimation error of the sideslip angle of the iAUKF under the three working conditions was less than 0.1°, 0.02°, and 0.5°, respectively. Based on the UDO and the GFTSMC, a vehicle yaw stability controller is described, which improves the accuracy of control and the robustness of the vehicle’s stability control system and greatly strengthens the driving safety of the vehicle.

Helicopter Turboshaft Engines’ Gas Generator Rotor R.P.M. Neuro-Fuzzy On-Board Controller Development

The work is devoted to the helicopter turboshaft engines’ gas generator rotor R.P.M. neuro-fuzzy controller development, which improves control accuracy and increases the system’s stability to external disturbances and adaptability to changing operating conditions. Methods have been developed, including improvements to the automatic control system structural diagram which made it possible to obtain the system transfer function in the bandpass filter transfer function form. The work also improved the fuzzy rules base and the neuron activation function mathematical model, which significantly accelerated the neuro-fuzzy controller training process. The transfer function frequency and time characteristics analysis showed that the system effectively controlled the engine and reduced vibration. Methods for ensuring a guaranteed stability margin and the synthesis of an adaptive filter were studied, which made it possible to achieve the system’s high stability and reliability. The results showed that the developed controller provided high stability with amplitude and phase margins, effectively compensating for changes in external conditions. Experimental studies have demonstrated that the control quality improved by 2.31–2.42 times compared to previous neuro-fuzzy controllers and by 5.13–5.65 times compared to classic PID controllers. Control errors were reduced by 1.84–2.0 times and 5.28–5.97 times, respectively, confirming the developed neuro-fuzzy controller’s high efficiency and adaptability.

Large-eddy simulation of shock train in convergent-divergent nozzles with isothermal walls

ABSTRACT High-order accurate Large-eddy simulations (LES) have been carried out for compressible, turbulent flow through two bell-shaped convergent-divergent nozzles- one with a short divergent portion and the other with a longer one. An explicit filtering approach based on approximate deconvolution method is used for the LES. The nozzles have isothermal walls and circular cross-sections. The incoming flow is a turbulent, subsonic fully developed pipe flow at centreline Mach number of around 0.4 and friction Reynolds number 216. An adaptive filter is used to capture the shocks appearing in the divergent portion of the nozzles. The complicated behaviour of the mean flow, Reynolds stresses, production and pressure-strain terms of the axial Reynolds stress budget in the shock train region is shown. The broadband nature of the shock train oscillations having multiple dominant frequencies in these nozzles is observed in the present LES.

Synthesis of hardware and software for adaptive discrete high-order control systems based on a model of the desired transient process with a guaranteed degree of stability

In this work, analytical expressions for the connection between optimization criteria andsettings parameters of the adaptive PI-PID controller with variable parameters of the object andadaptive filter have been removed. A method for synthesizing a model of discrete controllersbased on given dynamic characteristics has been developed. A method has been developed forsynthesizing a model of a discrete PID controller based on given dynamic characteristics basedon the criterion of the transient process of a guaranteed level of stability. 3 figures, 0 tables, 32formulas, 19 bibliographic sources..Analytical expressions have been removed to link the optimization criteria andadjustment parameters of the adaptive PI-PID controller with the variable parameters of theobject and adaptive filter.A method for synthesizing a model of discrete controllers from specified dynamiccharacteristics has been developed. The average warehouse of output pulses at the output of anonlinear channel, which is formed at the output of such a regulator and is seen as an inertialinduced uninterrupted part, changes according to the law, which is designated as a reversetransmission the function of the lanyard of the new molded filter model at the collar of the non-linear element.A method has been developed for synthesizing a model of a discrete PID controller fromspecified dynamic characteristics based on the criterion of the required transient process of aguaranteed stability level.The modeling showed that while the parameters of the object and the adaptive filter willchange during the ASC operation, then, obviously, the optimal parameters for adjusting theadaptive PID controller will immediately change.

Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter

The state of charge (SOC) is among the most crucial monitoring states in battery management systems. To accurately and robustly estimate the battery SOC, a closed-loop SOC estimation method based on deep neural networks is proposed in this study. Initially, a hybrid deep learning model, TGMA, is established by combining a temporal convolutional network (TCN) with a gated recurrent unit (GRU) and incorporating a multi-head self-attention mechanism (MHA) to estimate SOC. Subsequently, to further enhance the accuracy of estimating the battery SOC and the ability to resist outliers, the pre-estimated SOC from the deep learning model TGMA is used as the noisy observation in the Kalman filter, and robust factor and noise adaptive filtering are introduced into the Kalman filter, proposing a closed-loop estimation method based on deep learning and robust adaptive Kalman filter (RAKF). Finally, through comparison with other similar methods, the proposed TGMA-RAKF method is validated to have higher accuracy, generalization, and robustness against outliers under different operating conditions and temperatures, with root mean square error, mean absolute error, and maximum absolute error all less than 1 %, 1 %, and 2.5 % respectively.

Load Effect Analysis Method of Cable-Stayed Bridge for Long-Span Track Based on Adaptive Filtering Method

Load Effect Analysis Method of Cable-Stayed Bridge for Long-Span Track Based on Adaptive Filtering Method

Pansharpening Based on Multimodal Texture Correction and Adaptive Edge Detail Fusion

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected (TC) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the TC and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the TC image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the TC and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics.

Implementation of Massive Multiple-Input Multiple-Output (MIMO) 5G Communication System using Modified Least-Mean-Square (LMS) Adaptive Filters Algorithm

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters. The analysis parameters are channel noise, signal-to-noise ratio (SNR) and bit-error-rate (BER) using the hybrid precoder and combiner computation algorithms – Quantized Sparse Hybrid Beamforming (QSHB) and Hybrid Beamforming Peak Search (HBPS). Therefore, the proposed massive MIMO system model gives the less channel noise in the received signal, higher SNR and lower BER as compared to the traditional massive MIMO receiver systems.

A robust integrated navigation optimization method for USV in signal occlusion environment

Unmanned surface vehicles (USV) can use global navigation satellite systems (GNSS) and inertial navigation systems (INS) for combined positioning and navigation. However, buildings such as port facilities and bridges blocking GNSS signals will increase the error in the discriminator output in the GNSS vector tracking loop and reduce positioning accuracy. Meanwhile, due to the cumulative error in the inertial navigation system, the credibility of the navigation results when the signal is blocked is further reduced. In this regard, this study proposes a robust integrated navigation optimization method. Specifically, the RTS smoothing optimized Kalman filter is used to constrain the carrier phase error and code phase error output by the discriminator, which can dynamically adjust the gain of the vector tracking loop, thereby improving the signal tracking capability. Simultaneously, the prediction results of the gated recurrent unit (GRU) network optimized based on the attention mechanism are combined with the inertial navigation system to improve navigation accuracy. Furthermore, an adaptive Kalman filter is utilized as the integrated navigation filter. The actual path of the carrier refers to the navigation solution of the existing receiver. In the open environment, the proposed optimization method reduces horizontal positioning error and speed error by 44.7% and 37.1% respectively compared with existing methods. Simultaneously, it can effectively improve the robustness of positioning in signal obstruction environments. The proposed integrated navigation method provides new possibilities for optimizing USV navigation solutions.

Fast and accurate phase processing in off-axis digital holography combining adaptive spatial filtering and an embedded GPU platform

Abstract Parallel-phase processing enables rapid phase extraction from off-axis digital holograms. To achieve fast and accurate results, the phase reconstruction processes were parallelized using improved filter algorithms and optimized programming strategies. First, an adaptive filtering method based on the Chan-Vese (CV) model which better suits parallelism was designed to extract the +1 term spectrum. We selected suitable computer unified device architecture (CUDA) libraries according to the characteristics of the key phase reconstruction steps. Acceleration technologies, such as virtual memory and shared memory, were used to improve the computational efficiency. Furthermore, we combined an improved 4f optical imaging system with an embedded graphic processing unit (GPU) platform to design a low-cost phase reconstruction system for off-axis digital holography. To verify the feasibility of our method, the reconstructed quality of the CV filtering method was estimated, and the run times of phase retrieval on the central processing unit (CPU) and embedded GPU were compared for off-axis holograms with different pixel sizes. Additionally, the dynamic fluctuation phase maps of water droplet evaporation were retrieved to demonstrate the real-time capability of the method.

A modified feedforward-feedback time-frequency domain hybrid algorithm for multichannel active road noise control system

Multi-channel Active Road Noise Control (ARNC) systems require numerous adaptive filters to reduce noise at multiple positions inside the vehicle. However, road noise conditions are more complex, and the correlation between the reference signals and the target noise is often poor. When the driving conditions become slightly complex, the correlation between the reference signal and the target noise is often poor. As a result, traditional road noise control algorithms applied to multi-channel ARNC systems often suffer from high computational cost and poor noise reduction performance in non-steady-state and high-speed driving conditions. To address this issue,a reference signal selection method based on the genetic algorithm (GA) is proposed to obtain a reference signals combination with stronger coherence. On this basis, we introduce the time–frequency domain structure into the forward-feedback hybrid algorithm and a multi-channel time–frequency domain hybrid algorithm with a novel variable step size method (VSS-TF-HANC) is proposed in this paper.The feedforward part adopts a time–frequency domain structure and introduces a novel variable step-size strategy to reduce computational complexity and improve noise reduction performance. The feedback part adopts a control system based on the Internal Model Control (IMC) architecture to attenuate the interior noise components that have a strong response but poor correlation with the reference signals. Simulation results demonstrate that the proposed algorithm achieves excellent control results under complex driving conditions, with significantly better noise reduction performance compared to the control algorithms that solely employ the feedforward structure, while also exhibiting low computational cost.