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Optimal Decentralized Federated Density Fusion of GM-PHD Filters

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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Command-Filter-Based Fixed-Time Prescribed Tracking Switching Control for Nonlinear Systems With Unknown Control Coefficients

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第6期55卷 2844-2851页

作者： Hua, Changchun Pan, Wenlong Li, Hao Li, Qidong Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Peoples R China Hebei Univ Sci & Technol Inst Elect Engn Shijiazhuang 050018 Peoples R China

This article investigates fixed-time prescribed tracking control based on a command filter for a class of nonlinear systems with unknown control coefficients. A novel switching control mechanism is proposed to address the challenge of unknown control coefficients and introduce a dual-parameter switching strategy with online parameter adjustment based on the designed conditions. To address the limitation in existing research, where prescribed performance functions depend on the initial conditions of systems, this work designs a new class of prescribed performance functions that eliminates this dependency. A command-filter-based backstepping approach effectively avoids the computational complexity of high-order derivatives in traditional backstepping methods. In addition, the issue of the nondifferentiability of the virtual controller at switching moments in existing switching control methods has been resolved. Ultimately, the boundedness of all signals in the closed-loop system is ensured. Moreover, a simulation example of a second-order system verifies the effectiveness of the algorithm in this article.

关键词： Control systems Switches Nonlinear systems filtering theory Backstepping filtering algorithms filtering Control theory Target tracking System performance Command filter backstepping fixed-time prescribed tracking switching control unknown control coefficients

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Power-Enhanced GNSS Interference Mitigation With Sensed Equivalent Bandwidth Based on ASF Algorithm

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IEEE SENSORS JOURNAL 2025年第2期25卷 2886-2896页

作者： Song, Jie Lu, Zukun Xiao, Wei Li, Chenglong Wang, Yuqing Sun, Guangfu Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Key Lab Satellite Nav Technol Changsha 410073 Peoples R China Hunan Inst Adv Technol Yueyang 414015 Peoples R China

Power-enhancement is a primary focus in the global navigation satellite system (GNSS) to improve its anti-interference capabilities. However, interference issues cannot be fully resolved due to the limited increase in signal power. Additionally, traditional interference mitigation methods often misclassify signals as interference and suppress them, leading to significant GNSS signal loss. This article proposes an adaptive sparse filtering (ASF) method based on sensed equivalent bandwidth, enabling interference mitigation for GNSS power-enhancement without additional signal loss. By analyzing the spectral relationship between interference and signal bandwidth, the equivalent bandwidth that ignores the strong signal recognition is derived, and the sparse sampling model is developed to reconstruct the interference mitigation filter. Simulations and practical tests verify the effectiveness of this interference mitigation method, which is broadly applicable to both conventional and power-enhanced GNSS signals. It improves anti-interference performance by 3-5 dB without introducing additional computational complexity associated with spectral awareness. This improvement in signal quality also results in the 19% increase in positioning accuracy.

关键词： Interference Global navigation satellite system filtering algorithms Prevention and mitigation Signal processing algorithms Receivers filtering theory Satellites Information filters Vectors Adaptive sparse filtering (ASF) global navigation satellite system (GNSS) power-enhancement high anti-interference performance interference mitigation low-cost sensed equivalent bandwidth

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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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Incipient Fault Identification of Bearings in Electric Drive System Under Varying Speeds Based on Adaptive Feature Mode Decomposition

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IEEE SENSORS JOURNAL 2025年第9期25卷 15975-15995页

作者： Wang, Chaoge Tian, Xinyu Zhou, Funa Wang, Ran Li, Hongkun Shanghai Maritime Univ Sch Logist Engn Shanghai 201306 Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

Rolling bearings constitute a core component in the electric drive system of electric vehicles, and their health status is crucial for the safe operation of these vehicles. Therefore, effective condition monitoring and fault detection for bearings is of paramount importance. However, under variable operating conditions, the incipient weak fault signatures of bearings are prone to be masked by intense noise, significantly increasing the challenge of fault identification. To tackle this issue, an adaptive feature mode decomposition (AFMD) approach is introduced for diagnosing incipient weak fault in bearings under variable operating conditions. First, the instantaneous rotational frequency is extracted from the motor stator current signal. Subsequently, angular domain resampling is performed on the synchronously sampled vibration signal. Second, to overcome the lack of adaptability in determining the key input parameters of the FMD algorithm, which typically relies on repeated manual trials based on experience, this research proposes determining the number of decomposition modes through scale-space spectral segmentation. On this basis, the spectral Gini index (SGI) is adopted as the objective function, and the particle swarm optimization (PSO) is utilized to automatically ascertain the filter number and filter length. With the optimal decomposition parameter combination, the AFMD is employed to perform optimal mode decomposition on the obtained angular domain bearing signal, and the component exhibiting the highest SGI value is chosen as the sensitive mode. Finally, significant fault characteristic orders are extracted from the envelope order spectrum (EOS) of the sensitive component to accurately identify the fault type. The efficacy and superiority of the proposed methodology are confirmed through variable-speed simulated bearing signal, experimental data, and actual electric vehicle bearing diagnosis cases. The analysis demonstrates that the proposed approach can c

关键词： Vibrations Fault diagnosis Rolling bearings Noise Electric vehicles Accuracy filtering algorithms Finite impulse response filters Filter banks Feature extraction feature mode decomposition (FMD) incipient fault identification rolling bearing variable speed

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A Uniform Framework of Yau-Yau Algorithm Based on Deep Learning With the Capability of Overcoming the Curse of Dimensionality

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 339-354页

作者： Chen, Xiuqiong Sun, Zeju Tao, Yangtianze Yau, Stephen S. -T. Renmin Univ China Sch Math Beijing 100872 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China Beijing Inst Math Sci & Applicat Beijing 101408 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

In numerous application areas, high-dimensional nonlinear filtering is still a challenging problem. The introduction of deep learning and neural networks has improved the efficiency of classical algorithms and they perform well in many practical tasks. However, a theoretical interpretation of their feasibility is still lacking. In this article, we exploit the representational ability of recurrent neural networks (RNNs) and provide a computationally efficient and optimal framework for nonlinear filter design based on the Yau-Yau algorithm and RNNs. Theoretically, it can be proved that the size of the neural network required in this algorithm increases only polynomially rather than exponentially with dimension, which implies that the Yau-Yau algorithm based on RNNs has the ability to overcome the curse of dimensionality. Numerical results also show that our method is more competitive than classical algorithms for high-dimensional problems.

关键词： Filters Recurrent neural networks Probability density function Kalman filters filtering algorithms Covariance matrices Stochastic processes Curse of dimensionality nonlinear filtering recurrent neural networks (RNNs) Yau-Yau algorithm

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High-Quality Reconstruction of Depth Maps From Graph-Based Non-Uniform Sampling

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 780-791页

作者： Yang, Jingyu Xu, Wenqiang Hou, Yusen Ye, Xinchen Frossard, Pascal Li, Kun Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian 116024 Peoples R China Ecole Polytech Fed Lausanne Signal Proc Lab CH-999034 Lausanne Switzerland Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

Depth sensing is essential for intelligent computer vision applications, but it often suffers from low range precision and spatial resolution. To address this problem, we propose a novel framework that combines non-uniform sampling and reconstruction based on graph theory. Our framework consists of two main components: (1) a graph Laplacian induced non-uniform sampling (GLINUS) scheme that samples depth signals more densely around edges and contours than in smooth regions, and (2) an ensemble of priors (EoP) model that reconstructs the high-quality depth map using adaptive dual-tree discrete wavelet packets (ADDWP) transform, graph total variation regularizer, and graph Laplacian regularizer with color guidance. We solve the reconstruction problem using the alternating direction method of multipliers (ADMM). Our experiments demonstrate that our framework can capture fine structures and global information in depth signals and produce superior depth reconstruction results.

关键词： Image reconstruction Laplace equations Spectral analysis Color Task analysis filtering algorithms Image color analysis Graph signal processing non-uniform sampling depth reconstruction

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Outlier-Aware Recursive Instantaneous Minimum Error Entropy Algorithm

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 4076-4090页

作者： Zhong, Shan Peng, Bei Wang, Ziyi He, Jiacheng Zhang, Kun Wang, Gang Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

The minimum error entropy (MEE) criterion closely relies on the quadratic information potential (QIP) estimates of Renyi's entropy. Nevertheless, the conventional obtained QIP estimates are numerically unstable, especially when the error samples contain outliers, resulting in the optimal solution of the MEE criterion deviating from the target vector to a certain extent. To address this problem, we introduce the M-estimate method from robust statistics into the QIP estimation process;thus, a robust estimation method called instantaneous M-estimate QIP (IM-QIP) is proposed, and several important properties of IM-QIP are presented. The proposed IM-QIP estimates could be implemented simply while ensuring great robustness. Naturally, we further propose the corresponding instantaneous M-estimate MEE (IM-MEE) criterion and apply it to adaptive filtering. A robust recursive adaptive filtering algorithm, called the recursive IM-MEE (RIM-MEE) algorithm, is proposed and analyzed in this article, along with its stability and theoretical steady-state performance. Additionally, we demonstrate that the RIM-MEE algorithm achieves a smaller theoretical bound compared to the traditional MEE algorithm under heavy-tailed and skewed noise conditions. The simulation results revealed the robustness of the IM-QIP estimates and verified the theoretical expectations and superior performance of the RIM-MEE algorithm.

关键词： Estimation Signal processing algorithms Entropy Noise filtering Robustness Stability criteria filtering algorithms Probability density function Numerical stability M-estimate minimum error entropy (MEE) quadratic information potential (QIP) recursive adaptive filtering Renyi's entropy

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Advanced Adaptive Median Filter for Reducing Salt-and-Pepper Noise in GPR Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Wang, Wentian Du, Wei Li, Yabin Jia, Zhuo Inst Disaster Prevent Hebei Key Lab Earthquake Dynam Sanhe 065201 Peoples R China Yunnan Univ Sch Earth Sci Chenggong Campus Kunming 650106 Yunnan Peoples R China Jilin Univ Coll Geoexplorat Sci & Technol Changchun 130012 Jilin Peoples R China Changsha Univ Sci & Technol Sch Civil Engn Changsha 410114 Hunan Peoples R China

Due to the influence of both the observation environment and the instruments themselves, ground-penetrating radar (GPR) data are often contaminated by random noise, which degrades data quality. Salt-and-pepper noise is a common type of such noise. Adaptive median filtering is an effective technique for removing this noise. However, it has the drawback of replacing original values that are not affected by noise with the median, which can lead to a degradation in image quality. In this letter, we propose an improved adaptive median filtering method. First, we assess whether the original value is contaminated by salt-and-pepper noise. If the value is affected, filtering is applied. The window size is adaptively increased, and the window is subdivided into smaller sections. Multiple median calculations are then performed on the segmented windows to ensure the validity of the median. When the noise density is high, the median of the nonnoise points in the largest window is selected as the output, thereby minimizing the negative impact of noise on the median calculation. Both synthetic and real-world data validations demonstrate that the improved method significantly outperforms traditional adaptive median filtering, conventional median filtering, and other filtering methods, particularly in high-noise scenarios, thus confirming the superiority of the proposed algorithm.

关键词： Noise filtering Wavelet transforms Reflection Noise measurement Ground penetrating radar filtering algorithms Synthetic data Surface roughness Rough surfaces Adaptive median filtering ground-penetrating radar (GPR) random noise suppression signal processing

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Ant Colony Optimized Extended Kalman Filter for State of Charge Estimation of Lithium-Ion Batteries

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Kannan, M. Sundareswaran, K. Srinivasa Rao Nayak, P. Simon, Sishaj P. Mithun, T. LBS Coll Engn Dept Elect & Elect Engn Kasaragod 671542 Kerala India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 India Expert Global Solut Pune 431210 India

This work reports the practical implementation of ant colony optimized (ACO) extended Kalman filter (EKF) for state of charge (SoC) estimation of lithium-ion batteries (LiBs). The scheme is realized by employing a low-priced microcontroller. Two prominent parameters of EKF, namely, process (Q) and measurement (R) error covariances, are fine-tuned using the ACO algorithm. The problem of finding optimum values of Q and R together with SoC estimation is reformulated as an optimization problem. The proposed method has several advantages, such as computational simplicity, derivative-free operation, and near-optimal convergence with random initial guess. The performance, effectiveness, and robustness of the new SoC estimation are demonstrated for four different operating points of the tested battery. Measured mean square, mean absolute, and mean absolute percentage errors of 0.0165%, 1.2%, and 2%, respectively, with 99.34% R-squared value indicates that the proposed method is a promising candidate for the estimation of SoC of LiBs. Furthermore, the effectiveness of the ACO-tuned EKF approach is experimentally validated in comparison to the conventional EKF method.

关键词： Batteries Estimation Battery charge measurement Mathematical models Kalman filters Voltage measurement Temperature measurement Noise measurement Noise filtering algorithms Ant colony optimization (ACO) code composer studio (CCS) extended Kalman filter (EKF) lithium-ion battery (LiB) microcontroller state of charge (SoC)

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