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GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman filtering

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3700-3716页

作者： Buchnik, Itay Sagi, Guy Leinwand, Nimrod Loya, Yuval Shlezinger, Nir Routtenberg, Tirza Beng Gurion Univ Negev Sch Elect & Comp Engn ECE IL-84105 Beer Sheva Israel

Dynamic systems of graph signals are encountered in various applications, including social networks, power grids, and transportation. While such systems can often be described as state space (SS) models, tracking graph signals via conventional tools based on the Kalman filter (KF) and its variants is typically challenging. This is due to the nonlinearity, high dimensionality, irregularity of the domain, and complex modeling associated with real-world dynamic systems of graph signals. In this work, we study the tracking of graph signals using a hybrid model-based/data-driven approach. We develop the GSP-KalmanNet, which tracks the hidden graphical states from the graphical measurements by jointly leveraging graph signal processing (GSP) tools and deep learning (DL) techniques. The derivations of the GSP-KalmanNet are based on extending the KF to exploit the inherent graph structure via designing a graph frequency domain filtering and replacing the Kalman gain (KG) with a graph filter that minimizes the prediction error. Thus, it considerably simplifies the computational complexity entailed in processing high-dimensional signals and increases the robustness to small topology changes. Then, we use data to learn the KG, namely, the graph filter, following the recently proposed KalmanNet framework, which copes with partial and approximated modeling, without forcing a specific model over the noise statistics. Restricting the KG to a graph filter in the proposed GSP-KalmanNet reduces learned parameters, thereby enhancing stability. Our empirical results demonstrate that the GSP-KalmanNet achieves enhanced accuracy and run time performance, and improved robustness to model misspecifications compared with both model-based and data-driven benchmarks.

关键词： Complexity theory Computational modeling Kalman filters Frequency-domain analysis filtering algorithms Numerical models Heuristic algorithms Graph signal processing (GSP) graph filters deep learning Kalman filtering

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Application of Multidip Structure-Oriented filtering to Poststack Seismic Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Guo, Mengxin Chen, Siyuan Liu, Zuoshi Wang, Weihong Northeast Petr Univ Sch Earth Sci Heilongjiang Prov Key Lab Oil & Gas Geophys Explor Daqing 163318 Heilongjiang Peoples R China Northeast Petr Univ Natl Engn Res Ctr Offshore Oil & Gas Explorat Daqing 163318 Heilongjiang Peoples R China

In geophysics, structure-oriented filtering (SOF) has been extensively utilized as an effective algorithm to eliminate random noise. This type of filtering is mainly restricted by the local dip of the data and smoothed along the structural direction. The algorithm assumes the strata of a single local dip, and even if the seismic data contain strata and faults, the SOF remains smoothed with a single dip angle. This condition leads to the smoothing of faults or strata in the wrong direction, which results in the effective removal of reflected signals. Given this, we herein propose a multidip SOF (Mdip-SOF) algorithm for complex structure data, including faults. This investigation employs a guided filtering theory to pre-extract structural features of faults. Guided by this attribute, we construct an edge-preserving filter, where faults may exist, aiming to preserve faults and other structures. Other positions with a single dip are also adopted for conventional SOF. A poststack data denoising test is also conducted, and the results obtained reveal that the proposed multidip constrained SOF algorithm outperforms the existing SOF algorithm in terms of fault handling.

关键词： filtering Three-dimensional displays Noise reduction filtering algorithms Prediction algorithms Smoothing methods filtering theory Transforms Heuristic algorithms Image edge detection Guided filtering preserving faults random noise structure-oriented filtering (SOF)

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MPFNet: A Multiscale Phase filtering Network for Interferometric SAR

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Sun, Tao Wang, Zhen Ding, Zegang Zhao, Jian Zhu, Kaiwen Chen, Zhizhou Li, Han Beijing Inst Technol Sch Informat & Elect Radar Res Lab Beijing 100081 Peoples R China Beijing Inst Technol Minist Educ Key Lab Elect & Informat Technol Satellite Nav Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401331 Peoples R China

Phase filtering is one of the core signal processing steps in interferometric synthetic aperture radar (InSAR). In recent years, InSAR phase filtering algorithms have evolved from traditional solutions to deep learning (DL) methods, significantly improving the processing efficiency. However, most DL-based phase filtering techniques originate from optical filtering methods, and these methods inevitably entail a tradeoff between noise suppression and detail preservation. To resolve this contradiction and fully take into account the characteristics of InSAR phase, a multiscale phase filtering network (MPFNet) based on multilook information fusion is proposed. First, the network adopts the multiscale structure to balance noise suppression and detail preservation, where the multiscale information is obtained through multilook interferograms of varying numbers of looks. Second, drawing on the mechanism of super-resolution, the network incorporates the residual feature distillation blocks (RFDBs) to restore the scale of interferograms. Finally, in response to the demand for complex phase filtering, a loss function based on cosine similarity is constructed, which avoids the discontinuity at $\pm \pi $ affecting the filtering results. Computer simulation and experiments based on real InSAR data verified the effectiveness of the proposed method.

关键词： filtering Feature extraction Vectors Noise Training Optical filters Noise measurement Superresolution Indexes filtering algorithms Deep learning (DL) interferometric synthetic aperture radar (InSAR) multiscale phase noise filtering

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Parallelization Strategy of Non-Local Means filtering Algorithm for Real-Time Denoising of Forward-Looking Multi-Beam Sonar Images

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第12期34卷 13226-13243页

作者： Ye, Tao Deng, Xiangpeng Cong, Xiao Zhou, Hongkun Yan, Xiangming China Univ Min & Technol Beijing Sch Mech & Elect Engn Minist Emergency Management Beijing 100083 Peoples R China China Univ Min & Technol Beijing Key Lab Intelligent Min & Robot Minist Emergency Management Beijing 100083 Peoples R China China Ship Sci Res Ctr Wuxi 214028 Peoples R China

Obtaining clear sonar images is crucial for ocean exploration applications, such as marine resource detection and underwater target searches. Traditional filtering methods cannot effectively eliminate the noise generated by the complex underwater environment in sonar images and can potentially result in problems such as image blurring. Existing methods that effectively filter sonar image noise often lack real-time performance, making them impractical for ocean exploration. To address these limitations, this study proposes a real-time denoising technique for forward-looking multi-beam sonar images based on a non-local means filtering algorithm. The integral image is used to calculate the mean square error (MSE), which improves algorithm efficiency and ensures that the runtime remains unaffected by the neighbourhood window size. To further improve real-time performance, the algorithm is migrated to a graphics processing unit (GPU) and a block-wise computation method is proposed to calculate the integral image. Simultaneously, to enhance GPU thread utilisation, the three-dimensional thread structure from the compute unified device architecture (CUDA) programming model is utilised and additional threads are allocated to enhance computation. The captured images are filtered using an M1200d sonar device manufactured by Oculus. Extensive experiments demonstrate that the proposed method achieves excellent performance regarding both denoising accuracy and efficiency. Specifically, the proposed method achieves a peak signal-to-noise ratio higher than 25 dB and a structural similarity index of more than 0.85 at 50 frames per second, thus demonstrating its significant potential for real-time sonar image denoising.

关键词： Sonar filtering algorithms Real-time systems Graphics processing units Noise reduction filtering Mathematical models Sonar sensors non-local means filtering real-time processing GPU parallelisation

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An Algorithm Fusing State Estimation and TDOA filtering for UAV Tracking Enhancement

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 2251-2266页

作者： Zhang, Zhaohang Huang, Zhen Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In wireless sensor network positioning algorithms, tracking highly dynamic targets, such as unmanned aerial vehicles (UAVs), is challenging. In a poor communication environment, the accuracy of intermediate parameter estimation, such as the time difference of arrival (TDOA), is degraded, and the fact that the state transition equation for TDOA has no sequential solution makes it difficult to construct filtering algorithms. In this study, a data-driven multiple-model Kalman filter is proposed to enhance the tracking performance of highly dynamic UAVs in poor communication scenarios. The proposed algorithm builds an approximate model of motion states and constructs an augmented state transition model based on the measurements, which is applicable regardless of whether the state variables have an explicit sequential solution. In addition, considering the nonorthogonal characteristics between measurement noise and process noise, the process equation and Kalman gain of the proposed algorithm are derived to consider the influence of nonorthogonal terms, and the corresponding Cramer-Rao lower bound for the proposed algorithm is provided. Through simulation experiments, the convergence speed, filtering error, signal-to-noise ratio robustness, and computational complexity of the proposed algorithm are compared with those of traditional filtering algorithms, and its superiority in multiple-model motion scenarios is also verified using real-world data.

关键词： Location awareness filtering algorithms Wireless sensor networks Mathematical models filtering Signal to noise ratio Kalman filters Kalman filtering localization enhancement time difference of arrival (TDOA) tracking

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Iterative Self-Guided Image filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第8期34卷 7537-7549页

作者： He, Lei Xie, Yongfang Xie, Shiwen Jiang, Zhaohui Chen, Zhipeng Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411100 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Edge preserving filter is the basis of many computational photography and image processing. This can be achieved by global optimization method or local filtering method. Generally, the filtering results of global optimization methods are better than that of local filtering methods, and local filtering methods usually run much faster than global optimization methods. In this paper, a globally optimized method called iterative self-guided image filter (isGIF) is extended based on the assumptions of the guided image filter (GIF), which can produce high-quality edge-preserving filtering results by using the input image itself as the guidance image. Some comparisons with other edge-aware filters are presented to show the advantages of our method. Extensive experiments demonstrate that our filter generates images with better visual quality, while reducing/avoiding halo artifacts in the final image, and the running time is competitive.

关键词： Image edge detection Smoothing methods Information filters Optimization methods Nonlinear filters Maximum likelihood detection filtering algorithms Edge-preserving filtering guided image filter (GIF) global optimization local filtering methods

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Interbeat Interval filtering

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 481-485页

作者： Bayram, Ilker WHOOP Boston MA 02215 USA

Several inhibitory and excitatory factors regulate the beating of the heart. Consequently, the interbeat intervals (IBIs) vary around a mean value. Various statistics have been proposed to capture heart rate variability (HRV) to give a glimpse into this balance. However, these statistics require accurate estimation of IBIs as a first step, which can be challenging especially for signals recorded in ambulatory conditions. We propose a lightweight state-space filter that models the IBIs as samples of an inverse Gaussian distribution with time-varying parameters. We make the filter robust against outliers by adapting the probabilistic data association filter to the setup. We demonstrate that the resulting filter can accurately identify outliers and the parameters of the tracked distribution can be used to compute a specific HRV statistic (standard deviation of normal-to-normal intervals, SDNN) without further analysis.

关键词： Heart rate variability filtering algorithms Gaussian distribution Adaptive filters Standards Random variables Heart beat Computational modeling Adaptation models Accuracy Interbeat interval heart rate variability state-space robust filter probabilistic data association

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GFANC-Kalman: Generative Fixed-Filter Active Noise Control With CNN-Kalman filtering

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 276-280页

作者： Luo, Zhengding Shi, Dongyuan Shen, Xiaoyi Ji, Junwei Gan, Woon-Seng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Selective Fixed-filter Active Noise Control (SFANC) is limited by its selection of a single candidate from pre-trained control filters. In contrast, Generative Fixed-filter Active Noise Control (GFANC) addresses this limitation by employing an adaptive combination of sub control filters to generate more suitable control filters for different primary noises. However, GFANC solely relies on the information from the current noise frame to generate its control filter, resulting in potential inaccuracies when dealing with dynamic noises. Therefore, we propose a GFANC-Kalman approach that integrates an efficient one-dimensional convolutional neural network (1D CNN) with a Kalman filter to further improve the performance of GFANC. Specifically, the weight vector used to combine sub control filters is predicted by the 1D CNN for each noise frame, and then processed by the Kalman filter with minimal complexity. By considering the correlation between adjacent noise frames, the Kalman filter can enhance the accuracy and robustness of weight vector prediction. Hence, GFANC-Kalman is more able to adapt to changes in noise distribution, particularly for dynamic noises. Numerical simulations validate the efficacy of the proposed GFANC-Kalman approach in dealing with real-world dynamic noises.

关键词： Kalman filters Band-pass filters filtering theory filtering algorithms Information filters Real-time systems Covariance matrices CNN-Kalman filtering dynamic noise cancellation fixed-filter active noise control generative ANC

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Synthesis of Quasi-Optimal algorithms Based on the Indirect Method of Nonlinear filtering

Synthesis of Quasi-Optimal Algorithms Based on the Indirect ...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Vladimir Mikhaylovich Artyushenko Vladimir Ivanovich Volovach Evgeny Kimovich Samarov Information technology and management systems department Technological University Korolev city Russian Federation Informational and electronic service department Volga Region State University of Service Togliatty city Russian Federation Voronezh State University Voronezh city Russian Federation MIREA - Russian Technological University Moscow city Russian Federation Mathematics department St. Petersburg State Maritime Technical University St. Petersburg Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Nonlinear filtering of Markov processes by indi-rect variables is considered. Quasi-optimal algorithms with increased filtering accuracy in conditions of large fluctuations of filtered processes are synthesized. It is shown that the application of the indirect method of nonlinear filtering is more effective in comparison with the direct method which uses Gaussian approximation, where the observation model is an additive mixture of Gaussian noise and signal. At the same time, the indirect method successfully utilizes a functional approach. Dependencies allowing to estimate the phase error variance in the stationary mode on the phase excitation noise variance, as well as dependencies of the phase error skip prob-ability on the excitation noise variance at different values of the signal-to-noise ratio are obtained. It is shown that the consid-ered synthesis method allows to increase filtering accuracy of indirect algorithms synthesized in the first approximation, and the gain increases with the increase of the signal-to-noise ratio. At the same time, it is shown that in the stationary mode with limited phase stochasticity, the average convergence time of indirect estimates is three to four times shorter than the con-vergence time of direct estimates. It is noted that the indirect algorithm has a lower probability of phase error skip.

关键词： Accuracy Fluctuations filtering Gaussian noise Signal processing algorithms filtering algorithms Markov processes Approximation algorithms Gaussian approximation Signal to noise ratio

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Seizure Detection Algorithm Based on Hybrid Morphological filtering Optimization Multiscale Amplitude Integrated EEG

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IEEE SENSORS JOURNAL 2025年第8期25卷 13529-13540页

作者： Dong, Yongping Wu, Duanpo Jiang, Lurong Dong, Fang Liu, Junbiao Vidal, Pierre-Paul Cao, Jiuwen Wang, Danping Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Artificial Intelligence Inst Hangzhou 310018 Zhejiang Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China Hangzhou City Univ Sch Informat & Elect Engn Hangzhou 310015 Peoples R China French Natl Ctr Sci Res F-75016 Paris France Hangzhou Dianzi Univ Machine Learning & I Hlth Int Cooperat Base Zhejia Hangzhou 310018 Zhejiang Peoples R China Univ Paris Cite Plateforme Etud Sensorimotr PES BioMedTech Facil F-75270 Paris France

Automatic seizure detection algorithms have significant implications for improving patients' quality of life. However, the absence of multiscale analysis in these algorithms regarding electroencephalography (EEG) signals may lead to overlooking crucial seizure features. To address this limitation, our study aims to develop a novel seizure detection algorithm based on hybrid morphological filtering optimization multiscale amplitude integrated EEG (aEEG). First, multichannel EEG signals, collected by EEG sensors, are preprocessed with asymmetric filtering, rectification, envelope detection, multiscale time compression and signal smoothing to obtain multiscale aEEG. Second, morphological filter (MF), multiscale MF (MSMF), and average combination difference MF (ACDMF) are hybridized to filter, analyze, and reconstruct the signals, thereby emphasizing the seizures. Thereafter, optimal aEEG signal is selected using the channel and time compression scale (TCS) joint optimization algorithm based on the kappa (Ka), and this signal is utilized for seizure detection. Finally, it is evaluated on the CHB-MIT database using multiple validation methods, achieving satisfactory experimental results. The results indicate that the model attains accuracy (ACC), specificity (SPE), sensitivity (SEN), F1 score ( F1 ), and Ka of 97.60%, 96.56%, 98.63%, 97.60%, and 95.19%, respectively.

关键词： Electroencephalography filtering Feature extraction Sensors filtering algorithms Electronic mail Detection algorithms Optimization Smoothing methods filtering theory Electroencephalography (EEG) sensor signal hybrid morphology filter multiscale time compression optimal amplitude integrated EEG (aEEG) signal seizure detection

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