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IEEE OPEN JOURNAL OF POWER ELECTRONICS 2025年 6卷 734-746页

作者： Rigon, Saverio Haus, Benedikt Mercorelli, Paolo Zigliotto, Mauro Leuphana Univ Luneberg Inst Prod Technol & Syst IPTS D-21335 Luneburg Germany Univ Padua Dept Management & Engn I-36100 Vicenza Italy

A substantial reduction in the human environmental footprint can be achieved through the use of more efficient motors, such as synchronous reluctance motors (SynRM). As low-cost motors, SynRMs are commonly employed in sensorless AC drives. Sensorless algorithms based on the Extended Kalman Filter (EKF) offer several advantages, but they require a time-consuming trial-and-error tuning procedure. This paper proposes the automatic tuning of the EKF through a second Kalman Filter (KF) in a primary-secondary (PS) configuration. The two KFs work concurrently: the first estimates the required quantities for machine control, and the second updates the process noise statistics of the first KF. The second KF is much easier to tune, requiring only one non-critical parameter. Experimental results confirm the validity of this approach.

关键词： Tuning Kalman filters Main-secondary Estimation Noise Motors Technological innovation Covariance matrices Mathematical models filtering algorithms Kalman filter adaptive kalman filter sensorless synchronous reluctance motor (SynRM) AC machines primary-secondary Kalman Filter

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Bootstrap and High-Degree Cubature Particle Filters for Nonlinear Systems With Correlated Noise and Missing Measurements

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IEEE SENSORS JOURNAL 2025年第8期25卷 13219-13231页

作者： Zhang, Xing Yang, Zhenrong Lin, Xiaohui Xiang, Wenqian Guangxi Univ Sch Math & Informat Sci Nanning 530004 Peoples R China

In this article, the particle filtering problem is investigated for nonlinear systems with correlated noise and missing measurements (MMs). By accounting for both correlated noise and MMs, a novel explicit weighting expression is presented. Based on this weighting scheme, a new bootstrap particle filtering algorithm is designed to address such influence. Furthermore, to limit the particle degradation suffered by the bootstrap particle filter (PF), a novel importance function based on the Gaussian optimal filter is presented. To perform the numerical integration required by the Gaussian optimal filter, the fifth-degree spherical-radial cubature rule (FSRCR) is used to acquire a novel importance function. Consequently, a novel high-degree cubature particle filtering algorithm is developed for these systems. Simulation experiments show that the two proposed algorithms significantly improve estimation accuracy, with notable performance gains over the existing unscented Kalman filter (KF), especially as the sample size increases.

关键词： Noise Nonlinear systems Noise measurement Degradation filtering algorithms Particle measurements Atmospheric measurements Particle filters Maximum likelihood detection filtering theory Correlated noises fifth-degree spherical-radial cubature rule (FSRCR) importance function missing measurements (MMs) particle filter (PF)

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Optimal Decentralized Federated Density Fusion of GM-PHD Filters

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IEEE SENSORS JOURNAL 2025年第8期25卷 13420-13432页

作者： Yu, Xue Feng, Xi-An Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

To achieve the optimal uncertain multitarget decentralized tracking fusion, a federated density fusion algorithm of Gaussian mixture-probability hypothesis density (GM-PHD) filters is proposed. Our fusion algorithm requires a hierarchical structure. Each sensor node runs a local GM-PHD filter to extract multitarget state estimates from sensor measurements. Besides running a global filter that only predicts the fusion result of the previous moment, the fusion node also collects, associates, and merges the GM-PHDs of the global and local filters after eliminating the correlations among all filters. The association results can be judged as survival target detection, missed detection, target new birth, or target vanishing. Each association situation employs a different optimal merging method. To eliminate correlations resulting from feedback and common process noise, the covariance upper bounding technique is introduced. This technique returns the cross-covariances among all filters to zeros by increasing their auto-covariances and the process noise covariance. It is proven that using the global filter, feedback, and covariance upper bounding technique, the fusion node can obtain the same optimal accuracy as the centralized fusion. Simulations demonstrate that our algorithm, with appropriate parameters, can obtain the optimal tracking accuracy and outperforms the existing arithmetic average (AA) fusion algorithm.

关键词： filtering algorithms Merging Target tracking Sensor fusion Information filters Data mining Training Intelligent sensors Correlation Artificial intelligence Covariance upper bounding technique decentralized fusion Gaussian mixture-probability hypothesis density (GM-PHD) filter uncertain multitarget

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A Novel Set-Member Filter for Maneuver Non-Ellipsoid Extended Target Tracking With Unknown but Bounded Noises

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IEEE SENSORS JOURNAL 2025年第8期25卷 13708-13718页

作者： Ma, Tianli Sun, Hanying Wang, Kexin Chen, Chaobo Xian Technol Univ Autonomous Syst & Intelligent Control Int Joint Re Xian 710021 Peoples R China Xian Technol Univ Sch Elect Informat Engn Xian 710021 Peoples R China

For non-ellipsoid extended target tracking, the process disturbances and measurement noises are described by independent random variables, i.e., Gaussian distribution. In many real scenarios, however, the disturbances and noises are completely unknown except for bounds. This article proposes a novel set-member filter for maneuver non-ellipsoid extended target tracking with unknown but bounded (UBB) noise. The non-ellipsoid extent of the extended target is described by the random hypersurface model (RHM), the predicted state and shape parameters are obtained by calculate the Minkowski sum of the ellipsoids of the state and shape parameters and UBB noises, respectively. At the measurement update stage, the predicted shape parameters are modified based on the maneuver angle, and the minimal ellipsoid containing the pseudo-measurement nonlinear function is calculated by solving the semi-positive definite programming problem, in which the trace of the shape matrix of the pseudo-measurement nonlinear function is as the objective function. The update shape parameters are updated by calculating the intersection set of predicted shape ellipsoid and the minimal pseudo-measurement ellipsoid. An evaluation of performance in a simulated environment shows that the proposed SMF-NEET approach outperforms the existing adaptive extended Kalman filter based on a RHM (RHM-AEKF) and unscented Kalman filter based on the random hypersurface model (RHM-UKF) methods with up to 83.9% and 88.9% improvement in the accuracy in position, 40.3% and 58.2% in velocity, 26.6% and 31.5% in the shape, respectively, while tracking a maneuver non-ellipsoid extend target with UBB Noises.

关键词： Shape Noise Ellipsoids Target tracking Kinematics Noise measurement Shape measurement Kalman filters filtering algorithms Antenna measurements Extended target non-ellipsoid extension random hypersurface model (RHM) semi-positive definite programming set-member filter unknown but bounded (UBB) noises

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Convolutional Neural Network-Based Low Light Image Enhancement Method

COMPUTER OPTICS

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COMPUTER OPTICS 2025年第2期49卷 334-343页

作者： Li, M. X. Xu, C. J. Zhengzhou Univ Sci & Technol Inst Big Data & Artificial Intelligence Zhengzhou 450064 Peoples R China Zhengzhou Informat Engn Vocat Coll Informatizat Ctr Zhengzhou 451000 Peoples R China

With advances in science and technology, remote sensing images are vital for vegetation monitoring. The use of remote sensing allows for the collection of widespread, multi-temporal data on vegetation, leading to a better comprehension and management of natural resources. In this study, a new remote sensing image recognition model is proposed by combining the filtering algorithm to reconstruct the time series curve, fusing the quadratic difference method and decision tree, and introducing the morphological similarity distance method. The results of the experiment indicate that the normalized vegetation index in towns was consistently higher than the index in bodies of water throughout the year. The normalized index for water was generally close to or below 0. Additionally, the normalized index for forests surpassed that of both water and towns. Although the waveforms for all three were similar, the differences were significant. Notably, the forest normalized index curves had a single peak with a noteworthy duration. The study found that the mapping accuracy for fall plants was highest in 2013 (97.37 %) and lowest in 2014 (80.00 %). Similarly, for spring vegetation, the mapping accuracy was greatest in 2017 (97.96 %) and lowest in 2014, but still favorable at 90.00 %. These results highlight the high advantages of the remote sensing identification method proposed by the study for vegetation identification, which is crucial for natural resource management and environmental protection.

关键词： filtering algorithms time series curves remote sensing imagery normalized vegetation indices

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Learning Dynamic-Sensitivity Enhanced Correlation Filter With Adaptive Second-Order Difference Spatial Regularization for UAV Tracking

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 7211-7230页

作者： Yu, Yu-Feng Chen, Zhongsen Zhang, Yang Zhang, Chuanbin Ding, Weiping Guangzhou Univ Dept Stat Guangzhou 511370 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Zhaoqing Univ Sch Comp Sci & Software Zhaoqing 526061 Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China City Univ Macau Fac Data Sci Macau Peoples R China

Discriminative correlation filter (DCF)-based tracking algorithms continue to advance in the field of UAV tracking due to their computational efficiency. The idea of integrating the advantages of historical information and response adjustments into the CF tracking framework is continuously being developed. However, maintaining the stability of mobile video tracking in highly dynamic environments is extremely challenging. This difficulty arises from frequent changes in targets and backgrounds, as well as the stochastic noise generated by the photon-counting process in sensors. In addition, the inconsistent rates of these changes are often overlooked and require further scrutiny. In this paper, we propose a dynamic sensitivity enhanced correlation filter with adaptive second-order difference spatial regularization to address the issue of inconsistent motion rates in dynamic videos. We use the non-local means algorithm to denoise template images before feature extraction, improving the discriminative power of target contours. Then, we incorporate the proposed dynamic-sensitivity error method into CF learning and employ a novel adaptive second-order difference spatial regularization to simultaneously optimize the filter coefficients and spatial regularization weights. This regularization effectively works in synergy with the dynamic-sensitivity error strategy. Furthermore, an additional ADMM optimizer is introduced to derive the solution, thereby improving the convergence and computational efficiency of the algorithm. This algorithm supports the adjustment of filter updates in dynamic environments by balancing consistency with previous filter templates and flexibility to accommodate rapid target changes. By conducting extensive experiments on three challenging UAV tracking databases, we compare the proposed model with existing models. The experimental results demonstrate our superior performance. Code is released at: https://***/Johnsonirene/LDECF.

关键词： Target tracking Autonomous aerial vehicles filtering algorithms Correlation Feature extraction Accuracy Tracking Real-time systems Visualization Vehicle dynamics Correlation filters dynamic sensitivity UAV tracking non-local means second-order difference weights

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Distributionally Robust State Estimation for Highly Maneuvering Target Tracking With Model Uncertainty and Impulsive Measurement Outliers

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IEEE SENSORS JOURNAL 2025年第6期25卷 9876-9886页

作者： Zhang, Wenbo Song, Shenmin Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China

Due to the high maneuverability of the non-cooperative target and the complexity of the confrontation environment, the uncertainty of the tracking model and impulsive outliers in measurements degrade tracking accuracy and may even lead to complete loss of tracking. Existing research can hardly address these challenges. To ensure accurate tracking in the presence of uncertainty and impulsive measurement outliers (IMOs), we propose a distributionally robust state estimation (DRSE) method based on moment-based ambiguity sets. First, virtual maneuvering noise and a first-order Markov process are utilized to describe the maneuvering acceleration, while a set of independent and identically distributed random variables is used to characterize the interval length of IMO, thus constructing the process and measurement models, respectively. Then, the uncertainty of model is represented by moment-based ambiguity sets, and the state is estimated under the worst case conditional prior distribution. Furthermore, we employ an adaptive saturation mechanism to mitigate the impact of IMO, thereby ensuring robust-bounded-error state estimation in the presence of outliers. Finally, a glide trajectory of a typical hypersonic vehicle is established in this study. The numerical experiment results demonstrate the algorithm's effective handling of tracking model uncertainty and IMO.

关键词： Radar tracking Uncertainty Adaptive filters Noise filtering algorithms Accuracy Pollution measurement Mathematical models Adaptation models Distributionally robust impulsive measurement outliers (IMOs) model uncertainty moment-based ambiguity sets target tracking target tracking

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Multiplex Network-Based Approach to Gas Sensing

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IEEE ACCESS 2025年 13卷 67588-67598页

作者： Bhadola, Pradeep Khosla, Ajit Mahidol Univ Ctr Theoret Phys & Nat Philosophy Nakhonsawan Studiorum Adv Studies Nakhon Sawan 60130 Thailand Xidian Univ Sch Adv Mat & Nanotechnol Multimodal Sensing & Integrated Circuit Lab Xian 710126 Peoples R China

We present a multiplex network framework for analyzing sensor array data, integrating information from multiple network layers corresponding to different sensing conditions. Principal Component Analysis (PCA) is employed to reduce the dimensionality of the data and then used to construct the multiplex networks where each layer of the network is derived from correlation-based planar maximally filtered graphs (PMFG) for each distinct gas. The topological properties of the network layers are studied over different time frames. Clustering coefficients show a persistent local connectivity for ethanol and ethylene (stabilizing near 0.71) but a decreasing activity from 0.70 to 0.56 for acetone over time. We observe the highest path length for the initial phase of the sensor lifecycle, and a general decrease in path length is observed for most gases over time, indicating a decreasing sensor sensitivity. The analysis of multidegrees across different gases reveals both unique and shared connectivity patterns within sensor responses. Each gas shows some distinctive features in multi-degree, which decreases over time, and increase in significant overlapping features between gas combinations. For example, a high multidegree (001110) indicates simultaneous unique features in sensor response for ammonia, acetaldehyde, and acetone these features are not present in any other gas or their combination. We also observe a strong similarity in sensor response patterns between ethylene and ammonia. The shared features among certain gases indicate similar interaction patterns with sensors, potentially due to common chemical properties or response behaviors. We find that sensor drift significantly affects sensor responses, leading to evolving topological properties over time. Gas-specific network layers exhibit distinct trends, with some gases maintaining strong connectivity while others weaken due to drift. The methodology can be extended to other sensor arrays and applications beyond gas

关键词： Principal component analysis Gases Multiplexing Transmission line matrix methods Correlation Sensor arrays filtering algorithms Noise Eigenvalues and eigenfunctions Standards Multiplex network network theory gas sensors PCA sensor drift pattern recognition

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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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Imposing Correlation Structures for Deep Binaural Spatio-Temporal Wiener filtering

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1278-1292页

作者： Marvin Tammen Simon Doclo Department of Medical Physics and Acoustics and the Cluster of Excellence Hearing4all Carl von Ossietzky Universität Oldenburg Oldenburg Germany

To improve speech quality and intelligibility in environments with noise and interfering sounds, binaural speech enhancement algorithms use the microphone signals from both the left and the right hearing device to generate an enhanced output signal for each ear. As a multi-frame extension of the binaural multi-channel Wiener filter, in this paper we consider the binaural spatio-temporal Wiener filter (STWF) in the short-time Fourier transform domain, which requires estimates of the highly time-varying spatio-temporal correlations of the speech and interference components. To this end, the binaural STWF is embedded into an end-to-end supervised learning framework, where temporal convolutional networks estimate the required quantities, i.e., the inverse spatio-temporal correlation matrices of the interference component and the spatio-temporal correlation vectors and power spectral densities of the speech components. In this paper, we impose spatio-temporal correlation structure on these quantities and relate them between the left and the right hearing device, aiming to reduce computational complexity while maintaining speech enhancement and interaural cue preservation performance. Assuming that the spatial correlation of the speech component is stationary over a small number of frames, we propose to decompose the spatio-temporal correlation vectors as the Kronecker product of a relative transfer function vector and a temporal correlation vector, either considering a global reference microphone or a reference microphone for each hearing device. In addition, we consider a deep bilateral STWF by neglecting the spatio-temporal correlations of the speech and interference components between both devices. The imposed spatio-temporal correlation structures greatly differ in the number of parameters that need to be estimated. The performance of causal versions of the deep binaural and bilateral STWF algorithms is evaluated based on both simulated and measured binaural room imp

关键词： Correlation Microphones Speech enhancement Wiener filters Interference Auditory system Vectors filtering algorithms Noise Noise measurement

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