Time-varying Noise Research Articles

Dynamic state awareness for distribution network based on robust adaptive cubature kalman filter

With the rapid development of the economy and society, the demand for power quality is constantly increasing. As a crucial part of grid situational awareness, distribution network state estimation plays a vital role in providing critical data support for other advanced applications, which is significant for ensuring the safe and reliable operation of the distribution network. Therefore, this paper proposes a dynamic state awareness method for distribution networks based on the robust adaptive cubature Kalman filter (RACKF). The proposed solution, grounded on the core computational concept of the cubature Kalman filter (CKF), constructs a robust noise statistical estimator (NSE) composed of a biased NSE and an unbiased NSE to adapt to the unknown and time-varying process noise parameters in the dynamic state estimation process. The proposed solution can ensure that the calculated process noise parameters always meet the constraints and guarantee the robustness of the algorithm. In addition, an estimation strategy for the fusion of multi-time scale measurement data is developed according to the RACKF-based dynamic state estimation features in order to realize system state corrections and updates. The results of simulation experiments and comparisons with traditional CKF methods demonstrate the accuracy and superiority of the proposed solution.

Non-stationary mechanical sound source separation: An all-neural beamforming network driven by time–frequency convolution and self-attention

To tackle the challenging task of extracting target signal features amidst significant interference from complex, time-varying non-stationary noise in industrial scenarios, we propose a novel time-domain all-neural beamforming network tailored for non-stationary mechanical sound source separation, named TFANBNet. Leveraging time–frequency convolution and self-attention, TFANBNet promised robust performance in challenging acoustic environments. First, the proposed time–frequency convolution module employs parameterized complex-valued convolutional layers to simulate time–frequency transformations, thereby implicitly extracting time–frequency features. Then, the proposed adaptive attention transformer network enhances local attention interaction by integrating intermediate features, effectively enhancing long-range context modeling. Third, the beamforming module utilizes deep neural networks for beamforming weight estimation, replacing conventional noise covariance matrix computations, which have inherent performance limitations. Finally, the experimental results on the multi-channel spatial reverberation dataset synthesized from the MIMII dataset show that the proposed TFANBNet yields superior separation performance and generalization capabilities compared to the considered competitive methods.

Gravitational wave signal extraction against non-stationary instrumental noises with deep neural network

Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections in space would be more seriously affected by anomalies or non-stationarities in the science measurements. Considering the three types of foreseeable non-stationarities including data gaps, transients (glitches), and time-varying noise auto-correlations, which may come from routine maintenance or unexpected disturbances during science operations, we developed a deep learning model for accurate signal extractions confronted with such anomalous scenarios. Our model exhibits the same performance as the current state-of-the-art models do for the ideal and anomaly free scenario, while shows remarkable adaptability in extractions of coalescing massive black hole binary signal against all three types of non-stationarities and even their mixtures. This also provide new explorations into the robustness studies of deep learning models for data processing in space-borne gravitational wave missions.

Gaussian-inverse gamma mixture distribution–based extended Kalman observer for dynamic positioning control system of offshore platform

Under different working conditions and control objectives, fuzzy model predictive control is used to optimize the control effect by adjusting the weight to achieve stable and accurate control for the dynamic positioning control of offshore platforms. Also, aiming at the situation where the measurement noise of dynamic positioning control may be time-varying and non-Gaussian and the statistics of the noise are not exact, this paper proposes a variational Bayesian extended Kalman filter observer. In order to model non-Gaussian noise more reasonably, the joint posterior distribution of non-Gaussian noise with state and time-varying covariance is described as the product of Gaussian and independent inverse gamma distributions. Then, the variational Bayesian (VB) method was used to simultaneously estimate the system state, the intermediate variable values of the new distribution, and the noise parameters through fixed-point iteration. The fuzzy algorithm is applied to the model predictive control, and the weight parameters of the model predictive control are adaptively changed to better adapt to the changing working conditions in the control process. The simulation results show that the variational Bayesian extended Kalman filter observer has better performance than the existing algorithms when dealing with the time-varying non-Gaussian observation noise, whose statistics are not exact. The variational Bayesian extended Kalman filter observer can significantly improve the accuracy of the control when it is used in fuzzy model predictive control.

A self-supervised learning method for fault detection of wind turbines

Abstract As promising solutions to condition-based maintenance of wind turbines, artificial intelligence-based techniques have drawn extensive attention in the era of industry 4.0. However, accurate fault detection is still challenging owing to volatile operating conditions in real-world settings. To handle this problem, a novel method is proposed for fault detection of wind turbines. Specifically, a data augmentation scheme is developed to simulate the effects of time-varying environments and noise. Then, a self-supervised proxy task of variant prediction is designed and conducted. In this way, valid data representations can be extracted to represent the health status of wind turbines. Additionally, the compactness of data representations is guaranteed by the directional evolution, which can relieve the confusion of health conditions. The effectiveness of the proposed method is verified with actual measurements. Using the proposed method, several faults can be detected more than 10 d earlier, and blade breakage can be identified more than 22 h earlier. Furthermore, the developed method outperforms several benchmark approaches.

Adaptive Navigation Performance Evaluation Method for Civil Aircraft Navigation Systems with Unknown Time-Varying Sensor Noise.

During civil aviation flights, the aircraft needs to accurately monitor the real-time navigation capability and determine whether the onboard navigation system performance meets the required navigation performance (RNP). The airborne flight management system (FMS) uses actual navigation performance (ANP) to quantitatively calculate the uncertainty of aircraft position estimation, and its evaluation accuracy is highly dependent on the position estimation covariance matrix (PECM) provided by the airborne integrated navigation system. This paper proposed an adaptive PECM estimation method based on variational Bayes (VB) to solve the problem of ANP misevaluation, which is caused by the traditional simple ANP model failing to accurately estimate PECM under unknown time-varying noise. Combined with the 3D ANP model proposed in this paper, the accuracy of ANP evaluation can be significantly improved. This enhancement contributes to ensured navigation integrity and operational safety during civil flight.

The identification for time-varying parameter and noise of tuberculosis with seasonal changes based on data-behavior-driven system

The identification for time-varying parameter and noise of tuberculosis with seasonal changes based on data-behavior-driven system

Geometric approach to robust quantum control for time-varying noises

Geometric approach to robust quantum control for time-varying noises

Suppressing Thermal Noise to Sub-Millikelvin Level in a Single-Spin Quantum System Using Realtime Frequency Tracking.

A single nitrogen-vacancy (NV) center in a diamond can be used as a nanoscale sensor for magnetic field, electric field or nuclear spins. Due to its low photon detection efficiency, such sensing processes often take a long time, suffering from an electron spin resonance (ESR) frequency fluctuation induced by the time-varying thermal perturbations noise. Thus, suppressing the thermal noise is the fundamental way to enhance single-sensor performance, which is typically achieved by utilizing a thermal control protocol with a complicated and highly costly apparatus if a millikelvin-level stabilization is required. Here, we analyze the real-time thermal drift and utilize an active way to alternately track the single-spin ESR frequency drift in the experiment. Using this method, we achieve a temperature stabilization effect equivalent to sub-millikelvin (0.8 mK) level with no extra environmental thermal control, and the spin-state readout contrast is significantly improved in long-lasting experiments. This method holds broad applicability for NV-based single-spin experiments and harbors the potential for prospective expansion into diverse nanoscale quantum sensing domains.

Robust UKF orbit determination method with time-varying forgetting factor for angle/range-based integrated navigation system

The angle/range-based integrated navigation system is a favorable navigation solution for deep space explorers. However, the statistical characteristics of the measurement noise are time-varying, leading to inaccuracies in the derived measurement covariance even causing filter divergence. To reduce the gap between theoretical and actual covariances, some adaptive methods use empirically determined and unchanged forgetting factors to scale innovations within the sliding window. However, the constant weighting sequence cannot accurately adapt to the time-varying measurement noise in dynamic processes. Therefore, this paper proposes an Adaptive Robust Unscented Kalman Filter with Time-varying forgetting factors (TFF-ARUKF) for the angle/range integrated navigation system. Firstly, based on a statistically linear regression model approximating the nonlinear measurement model, the M-estimator is adopted to suppress the interference of outliers. Secondly, the covariance matching method is combined with the Huber linear regression problem to adaptively adjust the measurement noise covariance used in the M-estimation. Thirdly, to capture the time-varying characteristics of the measurement noise in each estimation, a new time-varying forgetting factors selection strategy is designed to dynamically adjust the adaptive matrix used in the covariance matching method. Simulations and experimental analysis compared with EKF, AMUKF, ARUKF, and Student’s t-based methods have validated the effectiveness and robustness of the proposed algorithm.

A novel residual-based Bayesian expectation–maximization adaptive Kalman filter with inaccurate and time-varying noise covariances

In this study, we introduce a novel residual-based Bayesian expectation–maximization adaptive Kalman filter (RBEMAKF) for dynamic state estimation with inaccurate and time-varying noise covariance matrices. The proposed scheme presents a novel maximum a-posteriori (MAP) estimator for characterizing process and measurement noises, leveraging the residual information derived from the Kalman filter. Simultaneously, the MAP is addressed through the application of the expectation–maximization (EM) algorithm. Subsequently, the standard Kalman filter is executed based on the estimated posterior of process and measurement noises (PMNs) to correct the dynamic state in real-time. Additionally, RBEMAKF is extended to propose Laplacian-RBEMAKF (L-RBEMAKF) and Student’s t-RBEMAKF (ST-RBEMAKF) to accommodate outlier environments. These methods assume Laplacian and Student’s t distributions for the prior of PMNs in RBEMAKF, respectively. Extensive simulations and real-world results demonstrate the effectiveness of the proposed RBEMAKF and its extensions (L-RBEMAKF and ST-RBEMAKF) in dynamic state estimation. The availability of our codes can be found at https://github.com/Gaitxh/RBEMAKF.

A micro-Doppler spectrogram denoising algorithm for radar human activity recognition

Radar signal recognition based on micro-Doppler spectrogram has been widely used in human action recognition tasks. However, in practical application scenarios, radar signals inevitably have noise, which leads to different degrees of deformation of the spectrogram graph structure and affects the accuracy of subsequent recognition algorithms. In this paper, we present “ACFL”, a novel algorithm for micro-Doppler spectrogram denoising, which aims to reduce the impact of noise on human action recognition. ACFL employs amplitude–frequency two-dimensional clustering and fuzzy logic clustering selection mechanism to remove noise elements from the spectrogram. Moreover, to address the issue of noise leakage or target missing under time-varying noise and action conditions, ACFL adopts spectrogram segmentation based on short-term Rényi entropy. By dividing the spectrogram into intervals with different time–frequency distributions, the dynamic spectrogram denoise over time is achieved. Simulation and measured data experiments demonstrate that the proposed algorithm not only achieves a higher-quality denoised spectrogram but also significantly improves the accuracy of human action recognition under noisy conditions.

Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter

The most significant issues for a thousand users is the unwanted or loud signals in sound files, it is impossible to eliminate or minimize these noise signals without knowledge their types and ranges. To overcome this issue, present an Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter (EIS-SNR-VBRAF) is proposed. Initially, the data is collected from Adaptive Myriad Filter using Time-Varying Noise and Signal-Dependent Parameters dataset. Then, the data is given for Pre-processing section, Variational Bayesian based Robust Adaptive Filter (VBRAF) is used to reduce the signal noise from Gaussian Noise, Traffic noise and Audio signal back ground noise. The proposed technique is implemented and efficacy of EIS-SNR-VBRAF technique is assessed by support of numerous performances such as BER, Signal-to-Noise-Ratio (SNR), Mean Squared Error, Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error, Structural Similarity Index, Cross-Correlation and Computational Complexity is analyzed. The proposed EIS-SNR-VBRAF method attains 21.18%, 23.52% and 23.65% lower RMSE, 21.52%, 21.76% and 23.24% higher PSNR, 21.19%, 21.73% and 20.48% higher Structural Similarity Index are compared with existing methods like early detection of mechanical malfunctions in vehicles utilizing sound signal processing (ED-MMV-SSP),noise reduction in infrasound signals based on mask coefficient binary weighting generalized cross correlation non-negative matrix factorization algorithm (NRID-MCWG-NMFA) and Click-event sound detection in automotive industry using machine/deep learning (CE-SDSI-DL) respectively.

A novel multi-source navigation algorithm based on factor graph in complex underwater environments of polar regions

Autonomous underwater vehicle (AUV) is an unmanned, cableless, self-propelled, autonomous navigation and control vehicle that maneuvers under unmanned or minimally monitored and intervened conditions. To solve the problem of increasing navigation accuracy error caused by time-varying measurement noise of AUV auxiliary sensors in complex underwater environments of polar regions, a multi-source navigation algorithm for AUV based on improved factor graph is proposed. The algorithm first adds a confidence function to the standard factor graph algorithm, which is used to adjust the covariance matrix of the navigation sensor to suppress the problem of time-varying measurement noise. Then, a sliding window is added to the factor graph algorithm and only nodes within the sliding window are optimized to ensure real-time performance. Finally, considering that inertial navigation has high accuracy in a short period of time, inertial navigation iterations are performed on nodes that are about to be marginalized within the sliding window to improve navigation accuracy. The analyses of the simulation experiment results show that the algorithm can effectively cope with the time-varying problem of measurement noise and ensure the accuracy and robustness of the AUV navigation system in the complex marine environment.

Quantifying noise effects in optical measures of excited state transport.

Time-resolved microscopy is a widely used approach for imaging and quantifying charge and energy transport in functional materials. While it is generally recognized that resolving small diffusion lengths is limited by measurement noise, the impacts of noise have not been systematically assessed or quantified. This article reports modeling efforts to elucidate the impact of noise on optical probes of transport. Excited state population distributions, modeled as Gaussians with additive white noise typical of experimental conditions, are subject to decay and diffusive evolution. Using a conventional composite least-squares fitting algorithm, the resulting diffusion constant estimates are compared with the model input parameter. The results show that heteroscedasticity (i.e., time-varying noise levels), insufficient spatial and/or temporal resolution, and small diffusion lengths relative to the magnitude of noise lead to a surprising degree of imprecision under moderate experimental parameters. Moreover, the compounding influence of low initial contrast and small diffusion length leads to systematic overestimation of diffusion coefficients. Each of these issues is quantitatively analyzed herein, and experimental approaches to mitigate them are proposed. General guidelines for experimentalists to rapidly assess measurement precision are provided, as is an open-source tool for customizable evaluation of noise effects on time-resolved microscopy transport measurements.

Autonomous navigation method of satellite constellation based on adaptive forgetting factors

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation, an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors (UKF-AFF) is proposed. The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors. The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors, equipping this method with more adaptability. The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues. Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations, the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively. Stability analysis of the proposed method is also conducted. The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments. The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30% and 44% respectively compared to those of the extended Kalman filter, and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

A generalized data assimilation architecture of digital twin for complex process industrial systems

As one of the critical cores of digital twin (DT), data assimilation (DA) can maintain consistency and synchronization between DT and physical system. Kalman filtering is a common DA method, but its estimation performance is deteriorated by factors such as model inaccuracy and time-varying noise covariance in practical applications. The errors caused by these multiple uncertainties are all coupled to the measurements, which augments the difficulty for DT to obtain physical system information. In order to tackle the DA problem with multiple uncertainties, this paper proposes a generalized DA architecture for DT in sophisticated process industry. First, by combining Stein variational gradient descent and nonlinear Bayesian filtering paradigm, a recursive estimation framework is established, which has higher accuracy in estimating the noise covariance compared to traditional methods. Second, to effectively deal with model inaccuracy by using filtering residuals containing time-varying noise, we propose a neural network and modified wavelet-based model error compensation (NNMW-MEC) block. Based on the modified wavelet technique, the filtering residual denoising built in NNMW-MEC can better cope with time-varying noise compared to existing wavelets, and extract the low-frequency signal involving model error information from noisy residual smoothly. In addition, because of the neural network-based state-compensation subblock, NNMW-MEC has more outstanding ability in compensating the state deviations with large changing range. Finally, we take the boiler system in a coal-fired power plant as an example to verify the effectiveness of our architecture. Experimental results show that the DA architecture proposed in this paper can improve the estimation performance of DT under inaccurate models and uncertain noise statistics.

Multirotor broadband noise modulation

Rotor broadband noise spectra are typically analyzed over time scales on the order of one or more rotor periods. However, modulation of the broadband noise spectrum with the blade passage frequency (BPF) has been shown to be significant for noise levels and perception of wind turbines and helicopters. In contrast, time-varying broadband noise has not been extensively studied for aircraft with many rotors, such as unmanned aerial vehicles (UAVs) or advanced air mobility aircraft. In this work, significant broadband noise modulation was measured in flight and anechoic chamber tests of hexacopter UAVs at various observer angles. This modulation is aperiodic with the BPF such that the modulation amplitude varies substantially between blade passages, even when the BPFs are controlled to be nearly constant between all rotors at all times. Furthermore, the azimuthal phasing between rotors greatly affects the measured modulation, such that the modulation of multiple rotors may be less than or greater than for a single rotor, depending on the phase offsets. The effects of phase variations on acoustic interactions between rotors is studied by comparing the sum of the modulation of individual rotors to the modulation of those rotors operating simultaneously. This is done not only using measurements, but also noise predictions made using PSU-WOPWOP. These results contribute understanding to how the noise modulation of rotors sum together, including the resulting directivity and aperiodicity.

Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs

Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions.

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach.

The integration of micro-electro-mechanical system-inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system's performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss-Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading's angular increment, and to fit the integrated navigation system's dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.