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A Denoising Method Based on Pulse Interval Compensation for High-Speed Spike-Based Image Sensor

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第8期31卷 2966-2980页

作者： Xu, Jiangtao Xu, Liang Gao, Zhiyuan Lin, Peng Nie, Kaiming Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Tianjin Univ Tianjin Key Lab Imaging & Sensing Microelect Tech Tianjin 300072 Peoples R China

In order to eliminate the noise on the pulse interval of high-speed spike-based image sensor, a pulse interval compensation (PIC) algorithm is proposed. Based on the analysis of the principle of sensor noise generation, a pulse interval error model is constructed and the compensation algorithm is developed. First, raw pulse data is obtained from the sensor system, and then the coefficient matrix is used to compensate the non-uniform pulse interval caused by the dark current. Then, the guided filter separates high-frequency noise from scenes details and the pulse interval compensation matrix is obtained in the corresponding pulse interval. Finally, the pulse interval compensation is performed on the raw pulse data for correction. In static scene experiments, the proposed method is compared with the existing state-of-the-art methods and shows a clear advantage in denoising in both low texture and rich texture scenes. In the experiment to film a turntable at a rotation speed of 2000 r/min, 3000 r/min, 6000 r/min, the average roughness decreases from 0.2830 to 0.2034, 0.2937 to 0.2139 and 0.3252 to 0.2501 respectively. These experiments proved that the proposed method can effectively eliminate the noise of the spike-based image sensor.

关键词： Image sensors Noise reduction Image reconstruction filtering algorithms Transforms Photodiodes Spike-based image sensor pulse interval error non-uniformity compensation algorithm image correction

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An Adaptive and Robust UKF Approach Based on Gaussian Process Regression-Aided Variational Bayesian

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IEEE SENSORS JOURNAL 2021年第7期21卷 9500-9514页

作者： Lyu, Xu Hu, Baiqing Li, Kailong Chang, Lubin Naval Univ Engn Dept Nav Engn Wuhan 430033 Peoples R China

In this study, both adaptive and robust one is proposed, considering that the properties of a classic unscented Kalman filter (UKF) can be degraded severely by the outliers measured in the contamination distribution and the effects of time-varying noise. An adaptive and robust UKF approach on Gaussian process regression- assisted Variational Bayesian is proposed. The Variable Bayesian (VB) method is used to statistically approximate the time-varying noise due to the strong nonlinearity and inaccuracy of the system model. At the same time, Gaussian Process Regression (GPR) has the advantage of machine learning. The observation information trained by the GPR model performs real-time prediction of the sliding window. Based on the above two points, the output source of the measurement information is estimated to realize the robustness of the filter measurement update. When the noise is uncertain and there are outliers in the measurement information, simulation and SINS/GPS integrated navigation of actual tests were performed, respectively. The test outcomes indicate that the algorithm has better nonlinear estimation performance than the traditional method, the effectiveness of the filtering algorithm proposed is verified.

关键词： Kalman filters Noise measurement Navigation filtering algorithms Adaptive systems Sensors Information filters Kalman filter adaptive Gaussian process regression integrated navigation robust unscented Kalman filter

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Research on Visual Tracking Algorithm Based on Peak Sidelobe Ratio

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IEEE ACCESS 2021年 9卷 105318-105326页

作者： Cui, Yin Ren, Hongge North China Univ Sci & Technol Coll Elect Engn Qinhuangdao 063210 Hebei Peoples R China Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China

Aiming at the problems of illumination changes, target deformation and background clutter in the target tracking field, a visual tracking algorithm based on peak sidelobe ratio is proposed. The object-interference model is used to represent the target appearance model, and context information is added to the relevant filtering framework. Training the filter internally to enhance the ability of filter discrimination. In the model update process, it is easier to introduce samples that cannot characterize the target, and use peak sidelobe comparison to update the tracking parameters, which can enhance the generalization ability of the model. Tested with some classic and recently algorithms in the OTB50, OTB100, UAV123, TC128 experimental video data set, the experiment' results show that the visual tracking algorithm that is proposed in the article can track the target more accurately. It has important research on the development of intelligent video surveillance value.

关键词： Target tracking Correlation Image color analysis filtering algorithms Visualization Information filters Histograms Target tracking machine vision correlation filter peak sidelobe ratio

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Object Detection-Tracking Algorithm for Unmanned Surface Vehicles Based on a Radar-Photoelectric System

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IEEE ACCESS 2021年 9卷 57529-57541页

作者： Yu, Qingze Wang, Bo Su, Yumin Harbin Engn Univ Sci & Technol Underwater Vehicle Lab Harbin 150001 Peoples R China

Object tracking is an important basis for the autonomous navigation of unmanned surface vehicles. However, several problems still must be addressed for a wide applicating of object tracking in unmanned surface vehicles. First, if multiple objects of the same classification exist in the same field of view, then stable extraction of an object is difficult. Second, in an environment with a complex background and large changes in object shape, the tracking accuracy is low, and object tracking errors and tracking loss can easily occur. Third, much time is required to detect a high-resolution real-time video stream, not meeting the delay requirement of the photoelectric servo stable tracking. To resolve these problems, this paper proposes an object detection-tracking algorithm based on a radar-photoelectric system. The algorithm combines an object detection algorithm with an object tracking algorithm and involves the following steps. First, a first-frame object extraction algorithm is used to extract the tracking object from the first frame. Second, a region of interest (ROI)-prediction algorithm is used to predict ROIs and detect objects in these ROIs. This algorithm can effectively solve the above problems in marine tests. When multiple objects of the same classification exist in the same field of view, the algorithm can extract the radar-guided object stably. When faced with a complex background and a large change in object shape, the algorithm substantially improves the accuracy and robustness of object tracking. Compared with the conventional object detection algorithm, the time consumption of this algorithm is reduced by 25.8%.

关键词： Radar tracking Object tracking Prediction algorithms Correlation filtering algorithms Classification algorithms Marine vehicles Unmanned surface vehicle radar-photoelectric system first-frame object extraction algorithm ROI-prediction algorithm object detection algorithm object tracking algorithm

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Structural Optimization for Asymmetrical Inline Topology Filter With Transmission Zeros Using Goal-Oriented Reinforcement Learning

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IEEE Access 2024年 12卷 111386-111399页

作者： Kiet Yew Leong Socheatra Soeung Sovuthy Cheab Cheng-Kai Lu Department of Electrical and Electronics Engineering Universiti Teknologi PETRONAS Perak Seri Iskandar Malaysia Cambodia Academy of Digital Technology Phnom Penh Cambodia Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

This paper presents a novel structural optimization approach for an asymmetrical inline topology with transmission zeros, known as Extracted Pole Unit (EPU) filter using a goal-oriented reinforcement learning method, specifically a hybrid of Soft Actor Critic (SAC) and Hindsight Experience Replay (HER). The recent popularity of reinforcement learning (RL) algorithms for optimizing cavity bandpass filters (BPFs) has led to several limitations. RL algorithms are inherently sample-inefficient, leading to prolonged model training times. A substantial number of training samples are required to achieve accurate results for a complex design using RL models. Additionally, most objective functions used for fitness calculations do not account for predistortion constants, which are crucial in synthesizing asymmetrical inline topologies with three transmission zeros as demonstrated in this work. To address these challenges, the proposed method incorporates predistortion-modified poles and transmission zeros within a feature-assisted objective function for use in the optimization process. Subsequently, a hybrid of SAC and HER is adopted as the optimization algorithm to leverage its improved sample efficiency by encouraging learning from diverse optimization scenarios and outcomes. The proposed method can optimize the self-couplings of the EPU filter in fewer optimization steps, showcasing enhanced training convergence speed and design accuracy.

关键词： Optimization Microwave filters Band-pass filters filtering theory Poles and zeros Linear programming filtering algorithms Neural networks Reinforcement learning Scattering parameters

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A Self-Adaptive Control for Phase-Controlled Electromagnetic Contactor Using Weighted Moving Average Filter

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第9期68卷 8963-8972页

作者： Fang, Shuhua Chen, Yong Lin, Heyun Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

This article proposes a self-adaptive control strategy for controlling the holding current of a novel phase-controlled contactor. The closing moment of contact can be captured by calculating the drop slope of current and the pulsewidth modulation is then applied to decrease the holding current. The weighted moving average filter is employed to filter the current and voltage signals. A novel approach using a quintic B-spline curve-fitting algorithm is proposed to determine the number of sampling values and their weights. Numerous theoretical analyses of electromagnetic force and magnetic flux density distributions are developed. A cosimulation coupling magnetic field, machinery, and circuit are carried out with the Maxwell and circuit editor software. A prototype is manufactured and the experimental results validate the effectiveness of the proposed self-adaptive control strategy for minimizing the holding current.

关键词： Actuators Splines (mathematics) Electromagnetics Pulse width modulation Iron Contacts filtering algorithms Phase controlled contactor pulsewidth modulation (PWM) self-adaptive control strategy weighted moving average filter

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Navigation Multisensor Fault Diagnosis Approach for an Unmanned Surface Vessel Adopted Particle-Filter Method

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IEEE SENSORS JOURNAL 2021年第23期21卷 27093-27105页

作者： Zhang, Chuang Cao, Chunyan Guo, Chen Li, Tieshan Guo, Muzhuang Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China Dalian Maritime Univ Marine Elect & Engn Coll Dalian 116026 Peoples R China

Fault diagnosis (FD) of unmanned surface vessels (USVs) is an important and challenging task for ensuring system safety and reliability. In this study, a robust navigation strategy with FD based on an adaptive particle-filter (PF) method is designed for USVs, with consideration of multiple fault modes involving sensors and propellers. This paper utilizes the switching-mode hidden Markov model to describe an unmanned vessel with probable fault modes. Then, to implement FD and robust navigation, an adaptive PF including self-evaluation algorithm and adaptive weight-adjustment method is run on the fault model. The self-evaluation algorithm is used to extend the posterior distribution, whereas the adaptive weight-adjustment method is used to enrich the particle diversity and process the particles with small weight. The proposed method is tested via sea trial to demonstrate the effectiveness of the fault model under failure of various sensors and propeller conditions. The results indicate that the proposed approach can realize better FD and generate more accurate state estimation, even when faults occur.

关键词： Hidden Markov models Global Positioning System Navigation Sensor systems Sensor phenomena and characterization Sea measurements filtering algorithms Unmanned surface vessel fault diagnosis adaptive particle filter sensors Markov model

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An Optimal Transport Formulation of the Ensemble Kalman Filter

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第7期66卷 3052-3067页

作者： Taghvaei, Amirhossein Mehta, Prashant G. Univ Illinois Urbana Champaign UIUC Champaign IL 61820 USA Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA Univ Illinois Urbana Champaign UIUC Coordinated Sci Lab Champaign IL 61820 USA Univ Illinois Urbana Champaign UIUC Dept Mech Sci & Engn Champaign IL 61820 USA

Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF) are numerical algorithms to approximate the solution of the nonlinear filtering problem in continuous time. The distinguishing feature of these algorithms is that the Bayesian update step is implemented using a feedback control law. It has been noted in the literature that the control law is not unique. This is the main problem addressed in this article. To obtain a unique control law, the filtering problem is formulated here as an optimal transportation problem. An explicit formula for the (mean-field type) optimal control law is derived in the linear Gaussian setting. Comparisons are made with the control laws for different types of EnKF algorithms described in the literature. Via empirical approximation of the mean-field control law, a finite-N controlled interacting particle algorithm is obtained. For this algorithm, the equations for empirical mean and covariance are derived and shown to be identical to the Kalman filter. This allows strong conclusions on convergence and error properties based on the classical filter stability theory for the Kalman filter. It is shown that, under certain technical conditions, the mean squared error converges to zero even with a finite number of particles. A detailed propagation of chaos analysis is carried out for the finite-N algorithm. The analysis is used to prove weak convergence of the empirical distribution as N -> infinity. For a certain simplified filtering problem, analytical comparison of the mse with the importance sampling-based algorithms is described. The analysis helps explain the favorable scaling properties of the control-based algorithms reported in several numerical studies in recent literature.

关键词： filtering algorithms Kalman filter Monte Carlo methods stochastic processes

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Distributed Kalman Filters With Random Sensor Activation and Noisy Channels

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IEEE SENSORS JOURNAL 2021年第24期21卷 27659-27675页

作者： Jin, Hao Sun, Shuli Heilongjiang Univ Sch Elect Engn Harbin 150080 Peoples R China

This paper studies distributed Kalman filtering problems for sensor networks with random sensor activation and noisy channels. Each sensor measures the target state with a random activation and receives state estimates of its neighbor nodes. Communication noises are considered in communication links between sensors and their neighbor nodes. Considering cross-covariance matrices between sensors, an optimal distributed Kalman filter (ODKF) with a given recursive structure is proposed for each sensor, which depends on the knowledge whether a sensor itself is activated to measure the target state or not and whose filter gains and covariance matrices depend on random activation rate of the sensor. In the ODKF algorithm, optimal Kalman filter gain for each sensor and different optimal consensus filter gains for state estimates of its neighbor nodes are designed in the linear unbiased minimum variance (LUMV) sense. To reduce computational cost, a suboptimal distributed Kalman filter (SDKF) is also presented by considering a locally minimum upper bound of the filtering error covariance matrix, where the calculation of cross-covariance matrices among sensor nodes is avoided. The stability and steady-state property of the proposed ODKF and SDKF algorithms are explored. Finally, the proposed methods are also extended to the nonlinear scenarios. Simulation examples show the effectiveness of the proposed algorithms.

关键词： Sensors Kalman filters Covariance matrices Estimation filtering algorithms Adaptive filters Sensor fusion Distributed Kalman filter random sensor activation noisy channels sensor network steady-state property

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FxRLS algorithms Based Active Control of Impulsive Noise With Online Secondary Path Modeling

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IEEE ACCESS 2021年 9卷 117471-117485页

作者： Jabeen, Fareeha Mirza, Alina Zeb, Ayesha Imran, Muhammad Afzal, Farkhanda Maqbool, Ayesha Natl Univ Sci & Technol NUST MCS Islamabad 44000 Pakistan Natl Univ Sci & Technol NUST CEME Islamabad 44000 Pakistan Natl Univ Sci & Technol NUST NBC Islamabad 44000 Pakistan

Active noise control algorithms undergo stability problems in the presence of impulsive noise. This paper investigates such algorithms with online secondary path modeling for impulsive noise and varying acoustic paths. The paper presents three methods for active noise control, along with improved online secondary path modeling. Firstly, the filtered x recursive least square algorithm is applied for both active noise control and online secondary path modeling. This method gave faster convergence, improved stability, and modeling accuracy as compared to existing ones. The filtered x recursive least square algorithm is not robust for abruptly changing acoustic paths. To overcome this problem another method that uses modified gain filtered x recursive least square algorithm for active noise control is presented. Furthermore, it is observed that modified gain filtered x recursive least square achieves the desired performance with overheads of increased complexity. Thus, a hybrid method is proposed which has less computational complexity than the rest methods with no compromise on active noise control system performance.

关键词： Adaptive filters filtering algorithms Convergence Computational modeling Computational complexity Acoustics Robustness Active noise control path modeling least square algorithms computational complexity

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