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Moving Human Target Tracking Method Based on Rotation Kernelized Correlation Filter for Through-the-Wall Radar

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4409-4420页

作者： Ma, Zeyu Qu, Xiaodong Zhang, Hao Meng, Haoyu Yang, Xiaopeng Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Yangtze Delta Acad Jiaxing 314000 Peoples R China

Tracking the position of human targets behind obstacles is one of the core functions of through-the-wall radar. Due to the fixed viewing angle of the through-the-wall radar and the complex scattering properties of the human targets, the radar images of human targets will rotate with the human movement, resulting in tracking difficulties. Aiming at solving this problem, a moving human target tracking method based on a rotation kernelized correlation filter is proposed. In the proposed method, the angle of the target region is estimated, and then, a rotation is performed on the region image to obtain the candidate target region. Next, a filter is utilized to perform correlation operation with the candidate region to locate the target. Finally, the training samples are extracted and a rotational update operation is performed to train a new filter. Numerical simulation and experimental results verify the tracking performance of the proposed algorithm.

关键词： Radar tracking Target tracking Radar imaging Radar Correlation filtering algorithms Accuracy Training Kalman filters Aerospace and electronic systems Human target tracking rotation kernelized correlation filter (RKCF) through-the-wall radar (TWR)

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An Adaptive Fault Feature Extraction Method for Aeroengine Intershaft Bearing Based on Casing Vibration

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IEEE ACCESS 2025年 13卷 35662-35673页

作者： Liang, Chen Xuan, Haijun Li, Guoqiang Zhang, Jiashuo Zhang, Dahai Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China Zhejiang Univ Coll Energy Engn Hangzhou 310027 Peoples R China Zhejiang Hiro Aviat Technol Co LTD Deqing 313219 Peoples R China

Fault diagnosis of the intershaft bearing of aeroengine is quite challenging because vibrations can only be measured on the distant external casing, resulting in weak fault features and complex interference. To address this issue, this paper proposes an adaptive multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) method to extract fault impulses of intershaft bearings based on casing signals. First, envelope harmonic product spectrum (EHPS) is used to estimate the bearing fault frequency and determine the parameter search range. Additionally, an evolved firefly algorithm (EFA) is introduced, incorporating mutation and step-size factors to enhance the convergence speed and global search capability of the algorithm. Furthermore, a new objective function EP is proposed that considers both the time-domain complexity and characteristics of periodic pulses, which is designed to enhance the capability to localize fault periodic impulses. Simulation results show that the proposed EPEFA-MOMEDA can accurately extract the fault period from simulated signals containing high-energy harmonics and random impulse noise without prior knowledge of the fault period. Finally, verification was conducted using real aero-engine casing vibration data. Comparative analysis demonstrates that this method is practical for engineering applications in extracting intershaft bearing faults from casing vibrations.

关键词： Deconvolution Aircraft propulsion Vibrations Feature extraction Harmonic analysis Optimization Fault diagnosis filtering algorithms Power harmonic filters Rotors Intershaft bearing aeroengine MOMEDA fault diagnosis feature extraction

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A Novel Mixture Least Squares Approach for Simultaneous Parameter/State and Unknown Input Estimation

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第2期70卷 1252-1258页

作者： Ding, Bo Wei, Yuanchu Zhang, Yong Yang, Wu Yangzhou Univ Coll Informat Engn Yangzhou 225127 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

In this article, we introduce a novel mixture least squares (MLSs) algorithm to deal with the problems of simultaneous parameter/state and unknown input estimation. First, the MLSs algorithm is derived to estimate the desired parameter and unknown input, which can be regarded as a unified framework for deterministic least squares and stochastic least squares. The unbiasedness and optimality of the MLSs estimators are further verified. Then, based on the established MLSs algorithm, a new solution to simultaneous state and unknown input estimation (SUIE) problems is given. The proposed method is more concise and straightforward than the existing SUIE algorithms. The method provided in this article offers fresh insight into parameter/state estimation with unknown input.

关键词： Covariance matrices Estimation Stochastic processes Maximum likelihood estimation Kalman filters Vectors filtering algorithms Linear stochastic systems mixture least squares (MLSs) parameter/state estimation unknown input estimation

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MusicReco: Interactive Interface Modeling With User-Centered Design in a Music Recommendation System

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IEEE ACCESS 2025年 13卷 30058-30087页

作者： Frantzvaag, Mats Ottem Chatterjee, Ayan Ghose, Debasish Dash, Soumya P. Kristiania Univ Coll Sch Econ Innovat & Technol N-5022 Bergen Norway STIFTELSEN NILU Dept Digital Technol N-2007 Kjeller Norway Simula Metropolitan Ctr Digital Engn Dept Holist Syst N-0170 Oslo Norway Indian Inst Technol Bhubaneswar Dept Informat & Commun Technol Bhubaneswar 752050 India

Recommendation technologies are widespread in streaming services, e-commerce, social media, news, and content management. Besides recommendation generation, its presentation is also important. Most research and development focus on the technical aspects of recommendation generation;therefore, a gap exists between recommendation generation and its effective presentation and user interaction. This study focuses on how personalized recommendations can be presented and interacted with in a music recommendation system using interactive visual interfaces. Interactive interface modeling with User-Centered Design (UCD) in a recommendation system is essential for creating a user-friendly, engaging, and personalized experience. By involving users in the recommendation process and considering their feedback, the system can deliver more relevant content, foster user trust, and improve overall user satisfaction and engagement. In this study, the visual interface design and development of a personalized music recommendation prototype (MusicReco) are presented using an iterative UCD approach, involving twenty end-users, one researcher, three academic professionals, and four experts. As the study is more inclined toward the recommendation presentation and visual modeling, we used a standard content-based filtering algorithm on the publicly available Spotify dataset for music recommendation generation. End-users helped to mature the MusicReco prototype to a basic working version through continuous feedback and design inputs on their needs, context, preferences, personalization, and effective visualization. Moreover, MusicReco captures the idea of mood-based tailored recommendations to encourage end-users. Overall, this study demonstrates how UCD can enhance the presentation and interaction of mood-based music recommendations, effectively engaging users with advancements in recommendation algorithms as a future focus.

关键词： Recommender systems Music User centered design Prototypes Iterative methods Conferences Software engineering Internet filtering algorithms Music recommendation system visualization user-centered design interface design iterative approach content-based filtering content-based filtering

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GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 1719-1733页

作者： Xia, Shuyin Wang, Cheng Wang, Guoyin Gao, Xinbo Ding, Weiping Yu, Jianhang Zhai, Yujia Chen, Zizhong Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Key Lab Cyberspace Big Data Intelligent Secur Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common rough set theoretical models. Although the PRS can use equivalence classes to represent knowledge, it is unable to process continuous data. On the other hand, NRSs, which can process continuous data, rather lose the ability of using equivalence classes to represent knowledge. To remedy this deficit, this article presents a granular-ball rough set (GBRS) based on the granular-ball computing combining the robustness and the adaptability of the granular-ball computing. The GBRS can simultaneously represent both the PRS and the NRS, enabling it not only to be able to deal with continuous data and to use equivalence classes for knowledge representation as well. In addition, we propose an implementation algorithm of the GBRS by introducing the positive region of GBRS into the PRS framework. The experimental results on benchmark datasets demonstrate that the learning accuracy of the GBRS has been significantly improved compared with the PRS and the traditional NRS. The GBRS also outperforms nine popular or the state-of-the-art feature selection methods. We have open-sourced all the source codes of this article at https://***/***, https://***/syxiaa/GBRS.

关键词： Rough sets Feature extraction filtering theory Computational modeling filtering algorithms Classification algorithms Training Attribute reduction feature selection granular ball granular computing neighborhood rough set (NRS)

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Pavement Potholes Quantification: A Study Based on 3D Point Cloud Analysis

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IEEE ACCESS 2025年 13卷 12945-12955页

作者： Sun, Qingzhen Qiao, Lei Shen, Yibo Zhengzhou Univ Aeronaut Sch Civil Engn & Environm Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Yellow River Inst Hydraul Res Zhengzhou 450001 Henan Peoples R China

Currently, the detection technology for road surface potholes, primarily focuses on the identification and segmentation, lacking the ability to quantitatively analyze the damage inflicted by road potholes. Therefore, this pa per proposes a method based on three-dimensional point clouds for the identification, segmentation, and reconstruction of road potholes, ultimately leading to the quantification of the damage volume. An RGB-D depth sensor is employed to collect point cloud data of road potholes. Voxel filtering and voxelization downsampling are used for denoising, filtering, and enhancing data processing efficiency. Surface segmentation is achieved through RANSAC (Random Sample Consensus) and Euclidean clustering, while the Alpha Shapes algorithm is utilized for three-dimensional volume reconstruction, facilitating the volumetric quantification of potholes. For evaluation, comparative experiments were conducted under different lighting conditions and shooting distances. The experimental results demonstrate that the proposed algorithm achieves an accuracy of 96.4% in volumetric damage measurement of road potholes, accurately determining the damage volume of pothole.

关键词： Point cloud compression Roads Shape Image reconstruction Volume measurement Surface morphology Maintenance filtering algorithms Cameras Information filters Potholes three-dimensional point cloud depth sensing camera surface segmentation

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Resilient Fusion of LMB Densities Based on Medoids

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IEEE SENSORS JOURNAL 2025年第6期25卷 10370-10379页

作者： Zhou, Yao Gao, Lin Li, Gaiyou Wei, Ping Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This article deals with the problem of resilient fusion of labeled multi-Bernoulli (LMB) densities, which arises in the situation that the sensor network (SN) undergoes abnormal behaviors like malicious attacks, resulting in the change of transmitted data from each sensor node. Compared to fusion algorithms based on perfect SN conditions, a detection procedure should be deployed before performing fusion so as to exclude abnormal data. To this end, we propose to decompose the LMB densities as the union of Bernoulli components (BCs), and then the medoids of BCs are exploited to form the fused LMB density. Besides, a new density-based spatial clustering of applications with noise (DBSCAN)-based label-matching algorithm is proposed. The performance of the proposed algorithm is verified via simulations.

关键词： Sensors Sensor fusion Clustering algorithms Target tracking Radio frequency filtering algorithms Noise Matched filters Information theory filtering theory Labeled multi-Bernoulli (LMB) medoid multiobject fusion random finite set resilient algorithm

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Distributed Active Noise Control Robust to Impulsive Interference

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IEEE SENSORS JOURNAL 2025年第7期25卷 11480-11490页

作者： Zhou, Yang Zhao, Haiquan Liu, Dongxu Guo, Xinnian Southwest Jiaotong Univ Sch Elect Engn Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Suqian Univ Jiangsu Prov Engn Res Ctr Smart Poultry Farming & Suqian Key Lab Visual Inspection & Intelligent Con Suqian 223800 Peoples R China

Traditional centralized and distributed active noise control (ANC) algorithms, which are based on the minimum mean-square error (MMSE) criterion, manifest substantial efficacy diminution when exposed to impulsive noise. To mitigate this challenge, we propose a novel diffusion filtered-x least mean M-estimate (DFxLMM) algorithm be used for sensor network-based multichannel ANC systems, which adaptively adjusts the threshold parameter in accordance with estimated variance of error signal, thereby effectively improving the resilience of the algorithm against impulsive noise perturbations. Moreover, to attain expeditious convergence and minimal steady-state misalignment, the implementation of a variable step-size DFxLMM (VSS-DFxLMM) algorithm is also proposed. In addition, an analytical discourse on the mean and mean-square behavior of the DFxLMM algorithm is presented. Simulation results demonstrate that the algorithms proffered herein outperform existing techniques in noise reduction efficacy for various types of noise under impulsive noise interference.

关键词： Noise Sensors Microphones Loudspeakers Filters filtering algorithms Convergence Wireless sensor networks Vectors Interference Active noise control (ANC) diffusion strategy impulsive noise modified Huber (MH) function wireless sensor network

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Robust Multichannel Decorrelation via Tensor Einstein Product

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 275-291页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Liu, Guannan San Jose State Univ Dept Appl Data Sci San Jose CA 95192 USA Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA Yuan Ze Univ Innovat Ctr AI Applicat Chungli 32003 Taiwan

Decorrelation of multichannel signals has played a crucial preprocessing role (in prewhitening and orthogonalization) for many signal processing applications. Classical decorrelation techniques can only be applied for signal vectors. Nonetheless, many emerging big-data and sensor-network applications involve signal tensors (signal samples required to be arranged in a tensor form of arbitrary orders). Meanwhile, the existing tensor-decorrelation methods have serious limitations. First, the correlation-tensors have to be of certain particular orders. Second, the unrealistic assumption of the specific signal-tensor form, namely the canonical polyadic (CP) form, is made. Third, the correlation-tensor has to be full-rank or an extra preprocessor based on principal component analysis is required for any non-full-rank correlation tensor. To remove the aforementioned impractical limitations, we propose a novel robust approach for high-dimensional multichannel decorrelation, which can accommodate signal tensors of arbitrary orders, forms, and ranks without any need of extra preprocessor. In this work, we introduce two new tensor-decorrelation algorithms. Our first new algorithm is designed to tackle full-rank correlation-tensors and our second new algorithm is designed to tackle non-full-rank correlation-tensors. Meanwhile, we also propose a new parallel-computing paradigm to accelerate our proposed new tensor-decorrelation algorithms. To demonstrate the applicability of our proposed new scheme, we also apply our proposed new tensor-decorrelation approach to pre-whiten the tensor signals and analyze the corresponding convergence-speed and misadjustment performances of the tensor least-mean-squares (TLMS) filter. Finally, we assess the computational- and memory-complexities of our proposed new algorithms by simulations over both artificial and real data. Simulation results show that our proposed new multichannel-decorrelation algorithms outperform the existing tensor-decorrel

关键词： Tensors Accuracy Computational modeling Simulation Signal processing algorithms Estimation filtering algorithms Vectors Decorrelation Convergence Tensor signal processing Einstein product multichannel decorrelation correlation tensor whitening eigenspread

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Two-Stage Correction for Wavefront Curvature Effects of PFA in Focusing Nonideal Circular SAR Data

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第1期61卷 886-899页

作者： Han, Shengliang Zhu, Daiyin Mao, Xinhua Hefei Univ Sch Adv Mfg Engn Hefei 230601 Peoples R China Nanjing Univ Aeronaut & Astronaut Nanjing 211106 Peoples R China

Although the polar-format algorithm (PFA) provides high computational efficiency in spotlight synthetic aperture radar imaging, it is still constrained to applications with large scene size requirements due to the unconsidered wavefront curvature effects. To solve this problem, a two-stage correction algorithm is proposed in this article to mitigate the image deterioration caused by planar wavefront approximation. Through analyzing the distribution characteristics of the quadratic wavefront curvature phase error (QWCPE) for a nonideal circular flight path with different elevation angles, the 2-D space-variant compensation problem is separated into two 1-D compensation. Based on the aforementioned analysis, the QWCPE is first assumed to be associated only with the distorted coordinates of the target, and the first-stage correction is applied to every range gate before the azimuth fast Fourier transform operation of the PFA. Then, the residual QWCPE after the first-stage correction (RQWCPE-1) is assumed to be associated only with the distorted azimuth coordinates of the target;to correct this error, the image after the first-stage compensation is separated into multiple subblocks along the azimuth direction;for each azimuth subblock, the RQWCPE-1 can be regarded as space invariant and corrected by constructing a filter according to the subblock center. Finally, all the subblocks are mosaicked together to generate the final refocused image. Simulated and real data experiments are performed to verify the performance of the proposed algorithm.

关键词： Azimuth Imaging Synthetic aperture radar filtering algorithms Approximation algorithms Trajectory Apertures Polar-format algorithm (PFA) subblock two-stage correction algorithm (TSCA) wavefront curvature effects

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