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An Adaptive Robust Cubature Kalman Filter Based on Sage-Husa Estimator for Improving Ship Heave Measurement Accuracy

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IEEE SENSORS JOURNAL 2023年第9期23卷 10089-10099页

作者： Zou, Tao Zeng, Weixiang Yang, Wenlin Ong, Muk Chen Wang, Yunting Situ, Weilun Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Guangdong Inst Intelligent Unmanned Syst Dept Tech Res & Dev Guangzhou 511458 Peoples R China Univ Stavanger Dept Mech & Struct Engn & Mat Sci N-4036 Stavanger Norway

High-pass and Kalman filtering are two digital filtering methods for ship heave acceleration signals. However, the traditional Kalman method has dynamic model errors due to the dependence on the fast Fourier transform (FFT) identification model. The presence of factors such as unknown statistical properties of the noise and anomalous measurement information at the same time can cause degradation in the accuracy of the heave solution or even lead to Kalman filter divergence. This study proposes an adaptive robust cubature Kalman filter based on the Sage-Husa noise estimator (SARCKF) for ship heave motion estimation to obtain higher heave measurement accuracy. Unlike the traditional method, which adjusts the process covariance matrix by prior knowledge, the new way estimates the statistical characteristics of noise online by Sage estimator, improving the real-time filtering performance. The Huber robust method is used to correct the measurement covariance matrix to suppress the outliers in the measurement information. Then, based on the strong tracking theory, an adaptive factor is derived and combined with the Huber method to form a new cost function, effectively compensating for the model error. The simulation results show that SARCKF can still make the estimation accuracy of heave displacement reach 5% of the maximum amplitude when the statistical characteristics of noise are unknown and the model has errors. In addition, the proposed method has stronger robustness than the traditional strong tracking filter (STF).

关键词： Kalman filters Adaptation models Sea measurements Marine vehicles filtering algorithms Band-pass filters Sensors Inertial measurement unit (IMU) robust cubature Kalman filter Index Terms Sage-Husa estimator ship heave measurement strong tracking filter (STF)

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A Novel Multilayer N-LMS Adaptive-Filter-Based Control for Synchronization and Power Quality Improvement in Grid-Tied SPV System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第9期70卷 9158-9168页

作者： Modi, Gaurav Singh, Bhim Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

In the power distribution grid, a solar photovoltaic (SPV) system may experience voltage sag, swell, harmonics distortion, unbalance, and dc offset at its point of interconnection. These conditions decline the power quality indices, which are escalated when the SPV system supplies power to unbalanced/distorted loads. In this context, in this article, a multilayer normalized least mean square adaptive-filter-based control is proposed for the grid-tied SPV system, where the sensed grid voltages and load currents are filtered using the proposed method. Then, their direct and quadrature (dq)-axis components are estimated and used to resolve synchronization issues. The estimated components are immune to negative-sequence components, distortion, low-order odd harmonics (3rd, 5th, 7th, and 9th), and dc offset. The proposed control scheme has a simple structure with two tuning parameters. Moreover, it has no integrator compared with the generalized-integrator-based algorithm and only has three delay units compared with the methods based on the cascaded delay signal cancelation (CDSC). Hence, it is computationally faster than the advanced CDSC and other multilayer-structure-based algorithms. The performance of the proposed method is demonstrated experimentally by implementing it on a laboratory setup of a grid-tied SPV system operating under nonideal voltages and currents.

关键词： Power harmonic filters Adaptive filters Harmonic analysis filtering algorithms Active filters Power filters Nonhomogeneous media Normalized least mean square (N-LMS) power quality (PQ) solar photovoltaic (SPV) system unbalanced/distorted loads unbalanced/distorted voltages weak grid

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Phase-Locked Time-Shift Data Augmentation Method for SSVEP Brain-Computer Interfaces

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 4096-4105页

作者： Mai, Ximing Ai, Jikun Wei, Yuxuan Zhu, Xiangyang Meng, Jianjun Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from ${40.88}\pm {4.54}$ bits/min to ${122.61}\pm {7.05}$ bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.

关键词： Training Training data Spatial filters Calibration Visualization filtering algorithms Data models Brain-computer interfaces (BCIs) data augmentation phase-locked time-shift (PLTS) steady-state visual evoked potential (SSVEP)

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An Indoor UWB NLOS Correction Positioning Method Based on Anchor LOS/NLOS Map

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IEEE SENSORS JOURNAL 2023年第24期23卷 30739-30750页

作者： Wang, Qing Li, Zehui Zhang, Hao Yang, Yuan Meng, Xiaolin Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Beijing Univ Technol Fac Architecture Civil & Transportat Engn Beijing 100124 Peoples R China

With the increasing demand for location-based services (LBS) in daily production and life, indoor positioning has gradually become one of the current research hotspots. The most pressing challenge in indoor positioning is achieving high-precision location in complex indoor environments. Aiming at the problem that severe non-line-of-sight (NLOS) interference with ultrawideband (UWB) positioning accuracy in complex indoor environments, this article proposes a novel indoor UWB NLOS correction positioning method based on anchor LOS/NLOS map, which utilizes prior environmental information. After anchors are deployed, the LOS/NLOS map is generated using the prior environmental information. Then, the line-of-sight (LOS) anchors are selected for position solution, and the observations under the influence of the NLOS anchors are corrected. Then, the trajectory is optimized using a map-modified Kalman filtering algorithm, and it adopts a method to solve the position of two LOS anchors by using trajectory constraints, which can deal with more complex indoor environment. The experimental results show that the NLOS correction method in this article has better accuracy than the traditional filtering algorithm, especially in NLOS environment with few anchors. Compared with the standard Kalman filtering algorithm, the average positioning accuracy is improved by 69.30%, and compared with the robust Kalman filtering algorithm, the average positioning accuracy is improved by 33.77%.

关键词： Distance measurement Nonlinear optics Kalman filters Indoor environment Sensors filtering algorithms Laser radar Indoor positioning line-of-sight (LOS)/non-line-of-sight (NLOS) map ultrawideband (UWB)

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Sparse Bayesian Learning for End-to-End EEG Decoding

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 15632-15649页

作者： Wang, Wenlong Qi, Feifei Wipf, David Paul Cai, Chang Yu, Tianyou Li, Yuanqing Zhang, Yu Yu, Zhuliang Wu, Wei South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China Pazhou Lab Guangzhou 510330 Guangdong Peoples R China Guangdong Univ Finance Sch Internet Finance & Informat Engn Guangzhou 510521 Guangdong Peoples R China Amazon Shanghai AI Lab Shanghai 200336 Peoples R China Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan 430079 Hubei Peoples R China Lehigh Univ Dept Bioengn Bethlehem PA 18015 USA Alto Neurosci Inc Loa Altos CA 94022 USA

Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matrix to encode both spatio-temporal filters and the classifier, all optimized under a principled sparse Bayesian learning (SBL) framework. Importantly, this SBL framework also enables us to learn hyperparameters that optimally penalize the model in a Bayesian fashion. The proposed decoding algorithm is systematically benchmarked on five motor imagery BCIEEG datasets (N = 192) and an emotion recognition EEG dataset (N = 45), in comparison with several contemporary algorithms, including end-to-end deep-learning-based EEG decoding algorithms. The classification results demonstrate that our algorithm significantly outperforms the competing algorithms while yielding neurophysiologically meaningful spatio-temporal patterns. Our algorithm therefore advances the state-of-the-art by providing a novel EEG-tailored machine learning tool for decoding brain activity.

关键词： Electroencephalography Decoding Classification algorithms Finite impulse response filters filtering algorithms Brain modeling Feature extraction Electroencephalography (EEG) brain-computer interface (BCI) emotion recognition decoding spatio-temporal filtering sparse Bayesian learning

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Infrared Small Target Tracking Based on OSTrack Model

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IEEE ACCESS 2023年 11卷 123938-123946页

作者： Shan, Jianhua Yang, Yu Liu, Heng Liu, Tao Anhui Univ Technol Sch Mech Engn Maanshan 243032 Peoples R China Anhui Univ Technol Sch Comp Sci & Technol Maanshan 243032 Peoples R China

Infrared small target tracking plays an important role in military reconnaissance, early warning, video surveillance, and civil applications. For tracking small infrared targets in this paper, a one-stream deep learning model is utilized. In order to integrate the processes of feature extraction and feature fusion, the model uses transformer as the framework's major component and creates a bidirectional information flow between the template and the search picture pairs in the feature extraction stage. Use the head of the model to get the target position. Finally, post-processing of the target area, including tracking success, saves the coordinate information of the target frame;tracking failure, near, in the middle, and far from the target box, searches for the real target. It helps to solve the situation where the target moves fast and encounters a complex background to achieve better tracking results. It is tested on an infrared small target data set, and the results show that the method in this paper reaches 80.50% average tracking accuracy. The image sequences in the data set include sky, sea, and buildings. Tracking video and original images are shown at https://***/AHUT507LAB/Infrared-dim-small-target-tracking.

关键词： Target tracking Feature extraction Transformers Correlation filtering algorithms Deep learning Optical fiber communication Infrared imaging Infrared small target small target tracking post-processing deep learning

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Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 3158-3173页

作者： Storch, Marcel de Lange, Norbert Jarmer, Thomas Waske, Bjorn Osnabruck Univ D-49074 Osnabruck Germany

The documentation of historical remains and cultural heritage is of great importance to preserve historical knowledge. Many studies use low-resolution airplane-based laser scanning and manual interpretation for this purpose. In this study, a concept to automatically detect terrain anomalies in a historical conflict landscape using high-resolution UAV-LiDAR data was developed. We applied different ground filter algorithms and included a spline-based approximation step in order to improve the removal of low vegetation. Due to the absence of comprehensive labeled training data, a one-class support vector machine algorithm was used in an unsupervised manner in order to automatically detect the terrain anomalies. We applied our approach in a study site with different densities of low vegetation. The morphological ground filter was the most suitable when dense near-ground vegetation is present. However, with the use of the spline-based processing step, all filters used could be significantly improved in terms of the F1-score of the classification results. It increased by up to 42% points in the area with dense low vegetation and by up to 14% points in the area with sparse low vegetation. The completeness (recall) reached maximum values of 0.8 and 1.0, respectively, when taking into account the results leading to the highest F1-score for each filter. Therefore, our concept can support on-site field prospection.

关键词： Vegetation mapping Remote sensing Cultural differences Support vector machines Laser radar Splines (mathematics) filtering algorithms Historical terrain anomalies machine learning splines UAV-LiDAR

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Gerchberg-Saxton Based FIR Filter for Electronic Dispersion Compensation in IM/DD Transmission Part II: Experimental Demonstration and Analysis

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第5期41卷 1428-1435页

作者： Wu, Xiong Karar, Abdullah S. Zhong, Kangping Gurkan, Zeynep Nilhan Lau, Alan Pak Tao Lu, Chao Hong Kong Polytech Univ Photon Res Inst Dept Elect & Informat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Amer Univ Middle East Coll Engn & Technol Egaila 15453 Kuwait Poet Technol Inc Shenzhen 518057 Peoples R China

In this article, the first experimental demonstration of a non-iterative electronic dispersion compensation (EDC) solution implemented at the transmitter using a finite impulse response (FIR) filter optimized with the Gerchberg-Saxton (GS) algorithm, is presented, for intensity-modulation and direct-detection (IM/DD) systems. The theoretical framework and preliminary simulations have been presented in Part I of this work. Here, the performance is compared between the GS FIR filter and the iterative GS algorithm in 56-Gb/s on-off keying (OOK) transmissions over 80-km single mode fiber (SMF) with a post feed-forward equalizer (FFE) for combating residual inter-symbol interference. Furthermore, the influence of the pulse shape (raised cosine or rectangular) and modulation format (return-to-zero (RZ) or non-return-to-zero (NRZ)) on the measured bit error ratio (BER) is investigated, while changing the number of FIR taps, post-FFE taps, and post-FFE samples-per-symbol. It is shown that within the range of the target digital extinction ratios (DERs) for which the original iterative GS algorithm offers benefit, both analytical and numerical methods for calculating the optimum FIR taps, outlined in Part I, produce similar BER performance as predicted. Hence, the former method is extended, here, through a non-recursive frequency response formula, which offers insight into the action of the GS filter with different pulse shaping and enables the derivation of the explicit GS impulse response. It is also shown that rectangular RZ exploits the full benefit of GS filtering through a uniform spectrum, achieving BER < 3.8x10(-3), with a 641-tap T/2-spaced pre-EDC FIR filter and a 3-tap adaptive T-spaced post-FFE.

关键词： Finite impulse response filters Modulation Dispersion filtering algorithms Signal processing algorithms Frequency-domain analysis Transfer functions Digital to analog converter digital signal processing electronic dispersion compensation finite impulse response filter Gerchberg-Saxton algorithm intensity modulation and direct detection Mach-Zehnder modulator

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PRkNN: Efficient and Privacy-Preserving Reverse kNN Query Over Encrypted Data

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2023年第5期20卷 4387-4402页

作者： Zheng, Yandong Lu, Rongxing Zhang, Songnian Guan, Yunguo Wang, Fengwei Shao, Jun Zhu, Hui Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada Zhejiang Gongshang Univ Sch Comp & Informat Engn Hangzhou 310018 Peoples R China

The advance of cloud computing has driven an emerging trend of outsourcing the rapidly growing data and query services to a powerful cloud for easing the local storage and computing pressure. Meanwhile, when taking data privacy into account, data are usually outsourced to the cloud in an encrypted form. As a result, query services have to be performed over the encrypted data. Among all kinds of query services, the reverse kNN query is highly popular in various applications, such as taxi dispatching and targeted push of multimedia information, but its privacy has not received sufficient attention. To our best knowledge, many existing privacy-preserving reverse kNN query schemes still have some limitations on the query result accuracy, dataset privacy, and flexible support for the choice of the query object and the parameter k. Aiming at addressing these limitations, in this paper, we propose an efficient and privacy-preserving reverse kNN query scheme over encrypted data, named PRkNN. Specifically, we first design a modified M-tree (MM-tree) to index the dataset and further present an MM-Tree based reverse kNN query algorithm in the filter and refinement framework. Then, we leverage the lightweight matrix encryption to carefully design a filter predicate encryption scheme (FPE) and a refinement predicate encryption scheme (RPE);and propose our PRkNN scheme by applying them to protect the privacy of the MM-Tree based reverse kNN query algorithm. Detailed security analysis shows that FPE and RPE schemes are selectively secure, and our PRkNN scheme can preserve both query privacy and dataset privacy. In addition, we conduct extensive experiments to evaluate the performance of our scheme, and the results demonstrate that our scheme is efficient.

关键词： Data privacy Encryption Servers Privacy Indexes Cloud computing filtering algorithms Encrypted data filter-refinement modified m-tree privacy preservation reverse kNN

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VI-HSO: Hybrid Sparse Monocular Visual-Inertial Odometry

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第10期8卷 6283-6290页

作者： Yang, Wenzhe Zhuang, Yan Luo, Dongting Wang, Wei Zhang, Hong Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Key Lab Robot & Comp Vis Shenzhen 518055 Peoples R China

In this letter, we present VI-HSO, a hybrid sparse monocular visual-inertial odometry system based on two innovative techniques called adaptive interframe alignment (AIA) and dynamic inverse distance filter (DIDF). Although the sparse image alignment algorithm appears efficient for calculating frame-to-frame motion, it tends to fail in case of significant intensity changes and motion blur. To overcome these limitations, we propose an adaptive interframe alignment method that allows for an adaptive selection between the original Lucas-Kanade (LK) method and the inverse compositional method when constructing photometric errors, along with the addition of inertial information in the process. This approach enables the tracking phase to utilize the full image and inertial information. During intense motion, the inverse distance of the new candidate point often fails to converge, leading to either scale drift or tracking failure. We present a dynamic inverse distance filter that can adjust the convergence range to update candidate points' inverse distance. This adjustment is based on the convergence ratio of the inverse distance of keyframes, which enables more convergent map points aiding in robust tracking in regions lacking texture and during rapid rotation. We evaluate the performance of VI-HSO on public datasets and real-world experiments, and our system outperforms state-of-the-art algorithms.

关键词： Odometry Tracking Estimation Pipelines Optical filters filtering algorithms Robustness Visual-inertial SLMA SLAM monocular vision

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