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USV Motion State Estimation by Using Credibility Theory in Improved GS-SRCKF

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第10期20卷 12072-12082页

作者： Wang, Yuanliang Ge, Quanbo Wang, Mengmeng Li, Hong Zhejiang Ocean Univ Sch Marine Engn Equipment Zhoushan 316022 Peoples R China Shanghai Maritime Univ Sch Logist Engn Shanghai 200135 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Jiangsu Prov Univ Key Lab Big Data Anal & Intelli Nanjing 210044 Peoples R China Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200082 Peoples R China Chinese Flight Test Estab Testing Inst Xian 710000 Peoples R China

A credibility-based and improved Gaussian sum square root cubature Kalman filter (IGS-SRCKF) estimation method for nonlinear non-Gaussian systems is proposed to improve the accuracy of unmanned surface vehicles (USVs) motion state estimation in complex environments. This method elevates the linear Gaussian credibility theory to the nonlinear non-Gaussian credibility theory by utilizing the improved Gaussian sum algorithm and constructing the square root cubature Klaman filter pseudomatrix, solving the problem of defining trust factors in nonlinear non-Gaussian systems, and measuring the credibility of the filter. At the same time, the conditions for designing high-performance filters are obtained using this credibility theory. Then, a new USV motion state estimation method based on credibility and the IGS-SRCKF framework is proposed by combining the hunter-prey optimizer and dingo optimization algorithm based on the obtained conditions for designing high-performance filters. Finally, the progressiveness and superiority of this algorithm are verified by simulation and actual data.

关键词： Kalman filters Accuracy Matrix decomposition Atmospheric modeling signal processing algorithms Oceans Covariance matrices Credibility theory dingo optimization algorithm (DOA) Gauss sum hunter-prey optimizer (HPO) nonlinear non-Gaussian unmanned surface vehicle (USV)

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UKF-FNN-RIC: A Highly Accurate UWB Localization Algorithm for TOA Scenario

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Zhang, Shuning Wang, Ershen Zhu, Zhanchen Yi, Jingyi Wang, Yongkang Kuai, Enze Shenyang Aerosp Univ Coll Aeronaut & Astronaut Shenyang 110136 Peoples R China Shenyang Aerosp Univ Coll Elect Informat Engn Shenyang 110136 Peoples R China Monash Univ Dept Data Sci & AI Melbourne Vic 3800 Australia

UWB positioning has become an important branch of indoor localization technology, which has high accuracy in the ideal state but is easily affected by the environment and other factors. To address the accuracy enhancement challenges of indoor UWB positioning across various conditions, a versatile UKF-FNN-RIC algorithm is proposed, leveraging the unscented Kalman filter (UKF), feedforward neural network (FNN), and redundant information correction (RIC). First, various existing UWB positioning algorithms are analyzed, the redundant information in the UWB solution model is studied in depth, and the relationship between the redundant information and the measurement vectors is clarified, thus the error correction algorithm of gradient descent of redundant information is proposed, and the data meets the preconditions of its application through UKF and FNN transforms. The UKF-FNN-RIC algorithm is proposed in time of arrival (TOA) scenarios. Finally, simulation experiments and practical experiments are carried out for two-way ranging (TWR) TOA scenario, which show that the algorithm in the article can greatly improve the accuracy and robustness. The accuracy of fixed point in the practical experiment reaches the millimeter level, the accuracy of track determination reaches the centimeter level, and the algorithms can be easily migrated to other positioning systems.

关键词： Accuracy Location awareness signal processing algorithms Kalman filters Distance measurement Base stations Heuristic algorithms Robustness Clustering algorithms Synchronization Feedforward neural network (FNN) gradient descent method redundant information correction (RIC) time of arrival (TOA) unscented Kalman filter (UKF) UWB localization

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DoDo: Double DOE Optical System for Multishot Spectral Imaging

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2024年第4期18卷 704-713页

作者： Urrea, Sergio Jacome, Roman Asif, M. Salman Arguello, Henry Garcia, Hans Univ Ind Santander Elect Engn Dept Bucaramanga 680002 Colombia Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA Univ Ind Santander Comp Sci Dept Bucaramanga 680002 Colombia

Snapshot Compressive Spectral Imaging Systems (SCSI) compress the scenes by capturing 2D projections of the encoded underlying signals. A decoder, trained with pre-acquired datasets, reconstructs the spectral images. SCSI systems based on diffractive optical elements (DOE) provide a small form factor and the single DOE can be optimized in an end-to-end manner. Since the spectral image is highly compressed in a SCSI system based on a single DOE, the quality of image reconstruction can be insufficient for diverse spectral imaging applications. This work proposes a multishot spectral imaging system employing a double-phase encoding with a double DOE architecture (DoDo), to improve the spectral reconstruction performance. The first DOE is fixed and provides the benefits of the diffractive optical systems. The second DOE provides the variable encoding of the multishot architectures. The work presents a differentiable mathematical model for the multishot DoDo system and optimizes the parameters of the DoDo architecture in an end-to-end manner. The proposed system was tested using simulations and a hardware prototype. To obtain a low-cost system, the implementation uses a deformable mirror for the variable DOE. The proposed DoDo system shows an improvement of up to 4 dB in PSNR in the reconstructed spectral images compared with the single DOE system.

关键词： Image reconstruction Encoding Imaging Optical sensors Optical imaging Image coding Optical filters Deep learning image coding image processing image reconstruction inverse problems hyperspectral imaging optical diffraction optical imaging signal processing algorithms

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The WQN Algorithm for EEG Artifact Removal in the Absence of Scale Invariance

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2663-2676页

作者： Dora, Matteo Jaffard, Stephane Holcman, David Ecole Normale Super IBENS UMR8197 PSL F-75005 Paris France Univ Paris Est Creteil Univ Gustave Eiffel CNRS LAMA UMR8050 F-94010 Creteil France

Electroencephalogram (EEG) signals reflect brain activity across different brain states, characterized by distinct frequency distributions. Through multifractal analysis tools, we investigate the scaling behaviour of several classes of EEG signals and artifacts. We show that brain states associated to sleep and general anaesthesia are not in general characterized by scale invariance. The lack of scale invariance motivates the development of artifact removal algorithms capable of operating independently at each scale. We examine here the properties of the wavelet quantile normalization (WQN) algorithm, a recently introduced adaptive method for real-time correction of transient artifacts in EEG signals. We establish general results regarding the regularization properties of the WQN algorithm, showing how it can eliminate singularities introduced by artefacts, and we compare it to traditional thresholding algorithms. Furthermore, we provide evidence that the algorithm performance is independent of the wavelet basis. We finally examine its continuity and boundedness properties and illustrate its distinctive non-local action on the wavelet coefficients through pathological examples.

关键词： Art Electroencephalography Time-frequency analysis signal processing algorithms Transient analysis Transforms Task analysis Artifact replacement EEG real-time algorithm regularity property transport wavelet decomposition

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Data-Driven Quickest Change Detection in (Hidden) Markov Models

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5567-5580页

作者： Zhang, Qi Sun, Zhongchang Herrera, Luis C. Zou, Shaofeng Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA Univ Buffalo Dept Elect Engn Buffalo NY 14228 USA

The paper investigates the problems of quickest change detection in Markov models and hidden Markov models (HMMs). Sequential observations are taken from a (hidden) Markov model. At some unknown time, an event occurs in the system and changes the transition kernel of the Markov model and/or the emission probability of the HMM. The objective is to detect the change quickly while controlling the average running length (ARL) to false alarm. The data-driven setting is studied, where no knowledge of the pre- or post-change distributions is available. Kernel-based data-driven algorithms are developed, which can be applied in the setting with continuous state, can be updated in a recursive fashion, and are computationally efficient. Lower bounds on the ARL and upper bounds on the worst-case average detection delay (WADD) are derived. The WADD is at most of the order of the logarithm of the ARL. The algorithms are further numerically validated on two practical problems of fault detection in DC microgrid and photovoltaic systems.

关键词： Hidden Markov models Kernel signal processing algorithms Fault detection Photovoltaic systems Microgrids Computational modeling Sun Numerical models Electrical fault detection Maximum mean discrepancy kernel method fault detection non-iid

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Sparsity and Periodicity-Induced Symplectic Geometry Decomposition for Incipient Fault Diagnosis of Wind Turbine Gearbox

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Zhang, Bo Teng, Wei Bai, Honghua Huang, Weiling Peng, Dikang Liu, Yibing North China Elect Power Univ Minist Educ Key Lab Power Stn Energy Transfer Convers & Syst Beijing 102206 Peoples R China Zhejiang Zhenxing Axiang Grp Co Ltd Huzhou 313008 Zhejiang Peoples R China

When incipient faults of gears or bearings occur in the wind turbine gearbox, their fault information will be readily masked by intensive background noise, which may postpone the fault detection and maintenance schedule. Specifically, the phenomenon manifested in the time domain is that the fault impulse is not obvious, while in the frequency domain, it is manifested that the fault frequency is prone to submerge in other interference frequency components. However, conventional signal processing methods and matrix factorization algorithms make it difficult to intuitively characterize fault features. Complex algorithms often require some manually adjusted parameters, which is difficult to be applied in engineering practice. To balance the performance and complexity of algorithms, a sparsity and periodicity-induced symplectic geometry decomposition (SPSGD) method is proposed. The initial components are obtained by the symplectic geometry mode decomposition method. Stem from the periodicity of gear faults in the time domain and the sparsity in the frequency domain, a periodicity-induced Bayesian clustering method is proposed as a novel iteration termination condition to pursue the fault-related components in the time domain. Then, a sparsity and periodicity-induced inverse filter is designed by the Laplace operator to weigh the faulty components to reduce the noise influence. The proposed SPSGD is validated by three cases, which indicate that the noise can be reduced adaptively as well as enhance the useful fault components effectively.

关键词： Noise reduction Noise Geometry Feature extraction Time-domain analysis signal processing algorithms Gears Vectors Bayes methods Wind turbines Fault diagnosis noise reduction sparsity and periodicity wind turbine

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Marginalized Beam Search algorithms for Hierarchical HMMs

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3013-3027页

作者： Xu, Xuechun Jalden, Joakim Royal Inst Technol Div Informat Sci & Engn KTH SE-10044 Stockholm Sweden

Inferring a state sequence from a sequence of measurements is a fundamental problem in bioinformatics and natural language processing. The Viterbi and the Beam Search (BS) algorithms are popular inference methods, but they have limitations when applied to Hierarchical Hidden Markov Models (HHMMs), when the primary interest lies in the outer state sequence. The Viterbi algorithm can not infer outer states without inner states, while the BS algorithm requires marginalization over prohibitively large state spaces. We propose two new algorithms to overcome these limitations: the greedy marginalized BS algorithm and the local focus BS algorithm. We show that they approximate the most likely outer state sequence with higher performance than the Viterbi algorithm, and we evaluate the performance of these algorithms on an explicit duration HMM with simulated and real nanopore base calling data.

关键词： signal processing algorithms Decoding Hidden Markov models Viterbi algorithm Search problems RNA Complexity theory hidden Markov models Viterbi beam search

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General Steganalysis of Generative Linguistic Steganography Based on Dynamic Segment-Level Lexical Association Extraction

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IEEE signal processing LETTERS 2025年 32卷 191-195页

作者： Li, Songbin Du, Hui Wang, Jingang Chinese Acad Sci Inst Acoust Hainan Branch Haikou 570100 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In scenarios where steganographic texts from various steganographic domains generated by different generative steganography algorithms are mixed, most existing linguistic steganalysis methods lack corresponding network structures designed to account for the differences in steganographic texts from different domains, leading to the potential for further improvement in their general detection performance. To address the above issue, we propose a general generative linguistic steganalysis method based on the basic idea of dynamically extracting lexical association features of different steganographic domains at the segment level. We utilize dynamic-static text feature matrix to construct a word importance semantic encoding module to mine steganography-sensitive word features of different steganographic domains. Based on the obtained features, we propose a word correlation multi-scale perception module to focus on the segment-level lexical association changes caused by secret information embedding in different domains. Experimental results show that this method can improve the detection accuracy of existing mainstream linguistic steganalysis methods in various mixed steganography scenarios.

关键词： Feature extraction Steganography Linguistics Convolution signal processing algorithms Correlation Semantics Payloads Training Data mining General detection generative linguistic steganalysis segment-level lexical association extraction

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Active Detection and Channel Estimation Schemes for Massive Random Access in User-Centric Cell-Free Massive MIMO System

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IEEE INTERNET OF THINGS JOURNAL 2024年第17期11卷 28078-28093页

作者： Hu, Yanfeng Wang, Qingtian Wang, Dongming Xia, Xinjiang You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Pervas Commun Res Ctr Nanjing 210096 Peoples R China China Telecom Res Inst Wireless AI Syst Res Team Wistar Beijing 100045 Peoples R China

The demand for higher transmission efficiency and denser user access has been put forth by the next generation of wireless communication systems. To cater to the future communication development, this article focuses on massive random access schemes under the user-centric cell-free massive multiple-input-multiple-output (MIMO) architecture. For uplink transmission, a data frame structure is designed to enable active user detection (AUD), channel estimation (CE), and data transmission. The association between access points (APs) and user equipment (UEs) is presented to facilitate an user-centric cell-free scalable architecture. In this article, a maximum likelihood (ML)-based method is proposed for AUD to obtain the set of active UEs. By setting appropriate thresholds and combining the UE-AP association, accurate active detection results can be obtained. CE can be accomplished with lower computational complexity by utilizing the detected active UE set in AUD module. Specifically, the generalized approximate message passing-based sparse Bayesian learning with Dirichlet process (GAMP-DP-SBL) is adopted as the CE algorithm, leveraging the spatial aggregation and dispersion characteristics of APs to enhance the estimation accuracy. Building upon GAMP-DP-SBL algorithm, a clustered algorithm (GAMP-CDP-SBL) is proposed to reduce the scale of the sensing matrix and improve the accuracy of CE for associated active UEs. Moreover, to enhance system scalability, decentralized AUD and CE algorithms are proposed in this article. Simulation results under various parameter settings and different scenarios exhibit the superior performance of the proposed scheme.

关键词： signal processing algorithms Clustering algorithms Channel estimation Massive MIMO Vectors Message passing Computer architecture Active user equipment (UE) detection cell-free massive multiple-input-multiple-output (MIMO) channel estimation (CE) compressed sensing (CS) massive random access

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Real-Time Wide-Range Carrier Frequency Offset Compensation by Multi-Thread PADE for Fully Softwarized Access Networks

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IEEE ACCESS 2024年 12卷 16915-16925页

作者： Suzuki, Takahiro Kim, Sang-Yuep Kani, Jun-Ichi Yoshida, Tomoaki NTT Corp NTT Access Network Serv Syst Labs Yokosuka Kanagawa 2390847 Japan

With the evolution of access systems by network function virtualization (NFV) and the software-defined network (SDN), further softwarization including transmission functions is required in order to fully utilize the advantages that softwarization offers to network operators in terms of more flexible and rapid service creation and migration. The softwarization of digital signal processing (DSP) is a prime target, but frequency offset compensation (FOC) has serial processing, which makes its softwarization difficult. In particular, the pre-decision-based angle differential estimator (PADE) of wide-range FOC needs long serial iterations since it obtains stable solutions by using small loop gain in the loop filter. This paper proposes a multiplication-based FOC implementation and a novel multi-thread PADE algorithm for real-time softwarization. The multiplication-based implementation enables parallel compensation of phase rotation by multiplying the estimated CFOs by sampling numbers for Viterbi-and-Viterbi (VV) method and PADE. In addition, our multi-thread PADE algorithm significantly reduces the iterations needed for FOC estimation;using multi-threading, it estimates multiple frequency offsets using the loop filters with large loop gain. This calculation needs relatively few iterations, but its outputs are very noisy, and final frequency offset estimates are obtained after removing the noise by clustering and averaging the results. The results of simulations and experiments, which use a general-purpose server with a graphic processing unit, show that our PADE algorithm is 1,308 times faster than the conventional implementation and 5-Gb/s real-time processing is achieved while maintaining the FOC performance.

关键词： Frequency estimation Real-time systems signal processing algorithms Digital signal processing Symbols Optical network units Software defined networking SDN PON softwarization DSP frequency offset compensation

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