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Application of Multidip Structure-Oriented filtering to Poststack Seismic Data

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 ...

引用

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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An Interpretable SAR Image filtering Algorithm

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Nurmamat, Pazilat Wan, Huiyao Chen, Jie Huang, Zhongling Yang, Lixia Li, Minquan Yang, Wei Zeng, Hongcheng Chen, Jie Diniz, Paulo S. R. Anhui Univ Natl Key Lab Optoelect Informat Acquisit & Protect Hefei 230601 Peoples R China China Elect Technol Grp Corp Res Inst 38 Hefei 230094 Peoples R China Northwestern Polytech Univ Sch Automat BRain & Artificial INtelligence Lab BRAIN LAB Shaa Xian 710000 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Fed Rio De Janeiro BR-21941972 Rio De Janeiro Brazil

Effective noise suppression is crucial for the subsequent interpretation tasks of synthetic aperture radar (SAR) imagery. Traditional SAR image processing techniques often overlook the coherent nature of noise, leading to a loss of vital detail during filtering. With advancements in deep learning (DL), significant strides have been made in image processing. However, existing DL methods do not fully leverage the imaging mechanisms of SAR, resulting in a lack of specificity and interpretability in the filtering process. To balance noise reduction with detailed preservation and to address the "black box" issue in filtering, we propose an interpretable filtering method that employs a correlation-based upward search for density peaks. Initially, we develop a MeanShift-Markov random field (MS-MRF) filter that integrates MeanShift with MRF in the joint spatial-spectral domain, ensuring both correlation and detailed preservation;the derivation of the MS-MRF filter is rigorously grounded in mathematical theory. Subsequently, we integrate MS-MRF with convolutional operations in DL to create a novel convolutional filter, interpretable MS-MRF convolution (IMMC), which enhances the model's interpretability, noise reduction capabilities, and detailed retention. Extensive experiments demonstrate that our method outperforms state-of-the-art (SOTA) SAR denoising techniques, achieving an average structural similarity index (SSIM) of over 85.00% and an average peak signal-to-noise ratio (PSNR) exceeding 35.00 dB across synthetic datasets with varying noise levels, showing significant improvements in noise suppression, detailed preservation, and interpretability.

关键词： Radar polarimetry Mathematical models Noise reduction filtering theory filtering algorithms Noise Speckle Information filters Correlation Synthetic aperture radar Deep learning (DL) interpretability speckle synthetic aperture radar (SAR) filter

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Vehicle Position Prediction Using Particle filtering Based on 3D CNN-LSTM Model

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2992-3004页

作者： Wang, Jiaqin Liu, Kai Gong, Yi Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Minist Informat Ind Beijing 100876 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Informat & Commun Syst Minist Informat Ind Beijing 100876 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Informat Ind Beijing 100876 Peoples R China

Vehicle position prediction (VPP) is of great significance for navigation planning and traffic safety of intelligent vehicles. In general, particle filtering (PF) uses global navigation satellite system (GNSS) to implement VPP. However, it does not consider geographic layer information (GLI) and its particle weight is not combined with the real-world geographic position information, which leads to insufficient prediction preparation. To resolve this problem, we propose a novel PF-based VPP method by using three-dimensional convolutional neural network and long short-term memory (3D CNN-LSTM) network model. First, for data preprocessing, we extract kinematic information features from GNSS, and evenly divide the area around each GNSS point into multiple grids and calculate the probability of grids center belonging to each GLI type. In addition, in order to better reflect the relationship between two consecutive positions due to the factors such as the conversion angle, we construct tilted cells to represent possible positions of each vehicle at any time. Second, a novel 3D CNN-LSTM model is designed to calculate the vehicle occurrence probability (VOP) in each tilted cell by processing the GLI and GNSS data, which can optimize the PF weight of each particle, and then improve PF to make more precise position prediction. Finally, the experimental results demonstrate that the proposed VPP method can improve the cell prediction accuracy, and then significantly improve the position prediction precision.

关键词： Feature extraction Global navigation satellite system Prediction algorithms Three-dimensional displays Convolutional neural networks Predictive models filtering algorithms 3D CNN geographic layer information LSTM particle filtering vehicle position prediction

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