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Millimeter wave imaging of Range Migration Algorithm with adaptive background filtering 认领 引用
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作者 CHENG Zhi-Hua ZHOU Ran +3 位作者 WANG Meng YU Tao WANG Yu-Lan YAO Jian-Quan 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2026年第2期279-284,共6页
This paper proposes a novel Range Migration Algorithm(RMA)integrated with an adaptive background filtering method specifically designed for near-field millimeter-wave imaging scenarios where targets are in close proxi... This paper proposes a novel Range Migration Algorithm(RMA)integrated with an adaptive background filtering method specifically designed for near-field millimeter-wave imaging scenarios where targets are in close proximity to background structures.This method simulates the attention distribution mode of the human visual system which is used in Artificial Intelligence(AI)and called the Attention Mechanism.Based on the concept of static clutter filtering,the frequency-domain signals of the scanning aperture are divided into grid cells.Background scattering functions are established by analyzing the motion processes within each cell,and the background interference is linearly filtered out.An analysis of the manifestation of background scattering interference within the algorithm is carried out,and the impact of the grid cell dimension on the imaging quality is investigated.Experimental results show that the proposed method exhibits the capability to enhance the signal-to-noise ratio of both the target and the background.It effectively suppresses the background interference,leading to a more prominent image,meanwhile without imposing the excessive computational load.The method offers a novel solution for improving the performance of millimeter-wave imaging technology in practical applications. 展开更多
关键词 information processing technology millimeter-wave imaging Range Migration Algorithm(RMA) attention mechanism adaptive background filtering
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A tracking algorithm based on adaptive Kalman filter with carrier-to-noise ratio estimation under solar radio bursts interference 认领 引用
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作者 ZHU Xuefen LI Ang +2 位作者 LUO Yimei LIN Mengying TU Gangyi 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期880-891,共12页
Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers... Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance. 展开更多
关键词 solar radio burst(SRB) global positioning system(GPS) adaptive Kalman filter(AKF) tracking algorithm.
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基于PSO-AEKF算法的钠离子电池SOC估计 认领 引用
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作者 张福建 王晓晗 +2 位作者 赵凯 晏娟 邓富金 《现代电子技术》 北大核心 2026年第8期65-70,共6页
当前电池荷电状态(SOC)估计研究主要集中于锂离子电池,而对钠离子电池关注不足。针对钠离子电池SOC估计中模型参数时变性与噪声干扰问题,提出一种融合粒子群优化(PSO)算法与自适应扩展卡尔曼滤波(AEKF)算法的协同估计方法。首先,基于二... 当前电池荷电状态(SOC)估计研究主要集中于锂离子电池,而对钠离子电池关注不足。针对钠离子电池SOC估计中模型参数时变性与噪声干扰问题,提出一种融合粒子群优化(PSO)算法与自适应扩展卡尔曼滤波(AEKF)算法的协同估计方法。首先,基于二阶RC等效电路模型,通过间歇放电实验标定OCV-SOC关系曲线;再结合HPPC工况,采用PSO算法在线辨识模型参数,实现模型动态特性精准表征。在此基础上,进一步设计AEKF算法,通过实时调整过程与测量噪声协方差矩阵,以提升算法对系统非线性及初始误差的鲁棒性。结果表明:PSO-AEKF算法SOC估计平均误差(MAE)为0.90%,均方根误差(RMSE)为1.28%,较传统EKF算法精度提升显著;同时针对不同初值SOC仿真的收敛时间小于20 s,验证了该方法的收敛稳定性及在复杂工况下的实用价值。 展开更多
关键词 钠离子电池 荷电状态 粒子群优化算法 自适应卡尔曼滤波算法 等效电路 参数辨识
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Variable Projection Order Adaptive Filtering Algorithm for Self-interference Cancellation in Airborne Radars 认领 引用
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作者 LI Haorui GAO Ying +1 位作者 GUO Xinyu OU Shifeng 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第4期497-508,共12页
The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is in... The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference. 展开更多
关键词 adaptive filtering algorithm airborne radar variable projection order mean squared deviation self-interference cancellation
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Stability analysis of distributed Kalman filtering algorithm for stochastic regression model 认领 引用
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作者 Siyu Xie Die Gan Zhixin Liu 《Control Theory and Technology》 EI CSCD 2025年第2期161-175,共15页
The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi... The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example. 展开更多
关键词 Distributed Kalman filtering algorithm Stochastic cooperative information condition Sensor networks (Lp)-exponential stability Stochastic regression model
基于EKF-MIUKF的锂电池SOC在线估算 认领 引用 被引量：1
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作者 莫蝶 解维锋 杨航 《能源研究与信息》 CAS 2026年第1期54-60,共7页
电池荷电状态(SOC)精确估计对提高新能源汽车电池管理系统的性能、电池使用安全性以及整车能量管理策略有效性具有至关重要的作用。采用二阶RC等效电路模型描述锂离子电池的动态特性,并通过实验获取SOC与开路电压(OCV)之间的关系。在SO... 电池荷电状态(SOC)精确估计对提高新能源汽车电池管理系统的性能、电池使用安全性以及整车能量管理策略有效性具有至关重要的作用。采用二阶RC等效电路模型描述锂离子电池的动态特性,并通过实验获取SOC与开路电压(OCV)之间的关系。在SOC估算过程中,为克服参数辨识中对噪声敏感的问题,运用扩展卡尔曼滤波(EKF)算法在线辨识模型参数;为实现对历史数据的重复使用,有效提高无迹卡尔曼滤波(UKF)在非线性系统中的适应性与收敛性,采用多新息无迹卡尔曼滤波(MIUKF)算法,并对MIUKF进行了分析与模型搭建,在城市道路行驶循环(UDDS)工况下对模型参数和SOC进行验证,并将结果与EKF、UKF算法的结果进行对比。结果表明,采用MIUKF算法估计锂电池SOC的误差控制在0.78%左右,估算误差小,鲁棒性好。 展开更多
关键词 锂离子电池 荷电状态(SOC) 扩展卡尔曼滤波(EKF) 多新息无迹卡尔曼滤波(MIUKF)
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基于EKF-LOCR-UKPF算法的电池SOC估计 认领 引用 被引量：2
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作者 韩瑞华 范兴明 张鑫 《桂林电子科技大学学报》 2026年第2期177-185,共9页
针对粒子滤波算法(PF)存在的粒子退化和单一滤波算法电池荷电状态(SOC)估计精度有限等问题,研究了一种基于二阶RC等效电路的宏观时间尺度下扩展卡尔曼(EKF)在线参数辨识和微观时间尺度下改进的粒子滤波算法(LOCRUKPF)状态估计相结合的... 针对粒子滤波算法(PF)存在的粒子退化和单一滤波算法电池荷电状态(SOC)估计精度有限等问题,研究了一种基于二阶RC等效电路的宏观时间尺度下扩展卡尔曼(EKF)在线参数辨识和微观时间尺度下改进的粒子滤波算法(LOCRUKPF)状态估计相结合的联合估计(EKF-LOCR-UKPF)算法。通过Simulink搭建EKF-LOCR-UKPF、LOCR-UKPF、UKPF和PF模型,并在联邦城市时间表(FUDS)和高速公路行车时间表(US06)工况下进行算法的仿真验证。仿真结果表明:考虑时间尺度、重要性密度函数和重采样策略的EKF-LOCR-UKPF算法在FUDS工况下,均方根误差较LOCR-UKPF、UKPF和PF算法分别降低了21.6%、30.7%、47.0%;在US06工况下均方根误差分别降低了36.9%、43.8%、55.4%。EKFLOCR-UKPF算法对电池SOC的估计精度有一定提升,在动力电池SOC预测及电池管理方面具有一定的应用价值和前景。 展开更多
关键词 电池SOC估计 粒子滤波算法 在线辨识 多时间尺度 联合估计
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基于EKF的丘陵山地拖拉机位姿信息解算试验 认领 引用
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作者 万起刚 董昊 +3 位作者 翟营 赵学平 闫祥海 徐立友 《农机化研究》 北大核心 2026年第12期251-259,共9页
针对丘陵山地拖拉机作业环境复杂导致其精准位姿信息获取受干扰的问题,开展了基于扩展卡尔曼滤波算法(EKF)的丘陵山地拖拉机位姿信息解算试验。首先,基于丘陵山地拖拉机状态融合位姿解算原理,建立了预测误差模型与数据融合状态预测更新... 针对丘陵山地拖拉机作业环境复杂导致其精准位姿信息获取受干扰的问题,开展了基于扩展卡尔曼滤波算法(EKF)的丘陵山地拖拉机位姿信息解算试验。首先,基于丘陵山地拖拉机状态融合位姿解算原理,建立了预测误差模型与数据融合状态预测更新模型;然后,搭建了东方红SH504M丘陵山地拖拉机实车数据采集平台和EKF,无迹卡尔曼滤波(UKF)算法环境;最后,通过30%标准桥试验和拖拉机丘陵山地作业环境颠簸试验,对EKF和UKF算法在不同参数配置下的预测效果进行对比,选择EKF算法并剔除部分时间段状态信息模拟恶劣环境下算法的预测效果,预测丘陵山地拖拉机状态变化。结果表明:EKF解算速度比UKF快,且两种算法参数设置在符合GPS/INS组合系统误差精度时,EKF算法估计的均方根误差和绝对误差均优于UKF;数据正常段,EKF算法预测的RMSE和平均绝对误差均达到1°级以内,最大预测误差为3.425°;数据剔除段,EKF算法预测的RMSE和平均绝对误差分别为2.012°、1.428°,最大预测误差为5.84°。试验结果验证了EKF算法在丘陵山地拖拉机位姿解算技术上的适用性和有效性。 展开更多
关键词 丘陵山地拖拉机 位姿信息解算 扩展卡尔曼滤波算法 状态预测 数据融合
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基于EKF和模糊控制的风力灭火机器人避障系统研究 认领 引用
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作者 王国歌 赵鑫 +3 位作者 丁禹程 曹川洋 张星皓 刘砚文 《林业机械与木工设备》 2026年第2期49-55,共7页
针对森林灭火及余火勘测作业中,履带式移动平台在非结构化复杂地形下存在的环境感知受限、单一传感器可靠性差等问题,设计了一种基于扩展卡尔曼滤波与模糊控制相结合的风力灭火机器人自主避障系统。首先,搭建了集成多线激光雷达与多路... 针对森林灭火及余火勘测作业中,履带式移动平台在非结构化复杂地形下存在的环境感知受限、单一传感器可靠性差等问题,设计了一种基于扩展卡尔曼滤波与模糊控制相结合的风力灭火机器人自主避障系统。首先,搭建了集成多线激光雷达与多路超声波传感器的硬件平台,并利用EKF算法对多源测距数据进行融合,有效弥补了单一传感器的局限性,获取了高精度的全局与局部障碍物距离信息。其次,充分考虑灭火机器人作业盲区及安全行驶约束,设计了具有“6输入-2输出”结构的Mamdani型模糊控制器。该控制器将融合后的多方位距离作为输入,实时输出左右履带的期望速度,以实现复杂工况的差速转向与平滑避障功能。最后,基于MATLAB仿真环境与实际野外路面条件,分别开展了避障算法验证与整机性能试验。结果表明,该系统能够准确识别多形态障碍物,并迅速做出减速与转向决策,在复杂环境下具有更高的测量精度、更快的响应速度与良好的避障鲁棒性,有效提升了风力灭火机器人的自主作业能力。 展开更多
关键词 风力灭火机器人 多传感器融合 扩展卡尔曼滤波 模糊控制
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基于FA-DSAEKF算法的车用动力电池荷电状态估计 认领 引用
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作者 康恒心 王计广 +3 位作者 许建忠 谭泽飞 李加强 易乾坤 《车用发动机》 北大核心 2026年第1期71-80,87,共10页
针对扩展卡尔曼滤波(EKF)在车用动力电池荷电状态(SOC)估计中存在的收敛速度慢、精度不高和鲁棒性较差的问题,提出了一种基于萤火虫算法优化的双对称自适应扩展卡尔曼滤波方法(FA-DSAEKF)。在EKF算法的基础上,通过智能优化初始参数、增... 针对扩展卡尔曼滤波(EKF)在车用动力电池荷电状态(SOC)估计中存在的收敛速度慢、精度不高和鲁棒性较差的问题,提出了一种基于萤火虫算法优化的双对称自适应扩展卡尔曼滤波方法(FA-DSAEKF)。在EKF算法的基础上,通过智能优化初始参数、增强算法对称性与稳定性,并实现噪声协方差矩阵的双参数自适应调整,显著提升了SOC估计性能。试验结果表明,在不同工况、温度与初始状态下,该算法均能快速稳定收敛,最大绝对误差、均方根误差和平均绝对误差均低于0.28%,收敛时间在200 s以内。相较于传统EKF算法,估计误差降低约80%,相较于DSAEKF算法,收敛速度提高83%以上,体现出优异的准确性、适应性和鲁棒性。 展开更多
关键词 车用动力电池 荷电状态 扩展卡尔曼滤波 等效电路模型 萤火虫算法
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基于自适应EKF的六相感应电机直接转矩控制研究 认领 引用
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作者 严凯 雷志宁 《自动化仪表》 CAS 2026年第7期64-68,共5页
针对六相感应电机中因相数增加导致的电磁耦合显著、转矩脉动及控制效果下降问题,开展基于自适应扩展卡尔曼滤波(EKF)的六相感应电机直接转矩控制方法研究。首先,构建Weibull分布模型计算绝缘安全裕度。设定六相感应电机的绝缘失效风险... 针对六相感应电机中因相数增加导致的电磁耦合显著、转矩脉动及控制效果下降问题,开展基于自适应扩展卡尔曼滤波(EKF)的六相感应电机直接转矩控制方法研究。首先,构建Weibull分布模型计算绝缘安全裕度。设定六相感应电机的绝缘失效风险阈值。以此阈值为依据,调整直接转矩控制参数,从而确保电机运行的稳定性。然后,构建电机状态方程预测当前状态。最后,设计并扩展实时估计电机状态。利用EKF算法迭代修正定子磁链真实幅值与位置角,以实现直接转矩的动态控制。试验结果表明,该方法使磁链轨迹接近圆形,定子磁链变化、定子电流变化结果均与实际相符。该方法可有效控制电机直接转矩,具有较高的应用价值。 展开更多
关键词 六相感应电机 自适应 扩展卡尔曼滤波 绝缘可靠性 强非线性 直接转矩控制
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一种卡方检验EKF在紧组合导航中的应用 认领 引用
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作者 杨一璠 张奇志 罗艳敏 《导航定位学报》 CSCD 北大核心 2026年第1期100-107,共8页
针对捷联惯性导航系统(SINS)误差随时间累计及北斗卫星导航系统(BDS)抗干扰能力弱的问题,采用一种SINS与BDS紧组合的导航方案,以提高导航系统长时间工作的精度、抗干扰能力和稳定性。针对非线性组合导航系统中建模不准确引起的新息失配... 针对捷联惯性导航系统(SINS)误差随时间累计及北斗卫星导航系统(BDS)抗干扰能力弱的问题,采用一种SINS与BDS紧组合的导航方案,以提高导航系统长时间工作的精度、抗干扰能力和稳定性。针对非线性组合导航系统中建模不准确引起的新息失配问题,提出一种指数软卡方检验自适应扩展卡尔曼滤波算法(EKF):通过设置阈值判断新息是否异常,按新息异常程度使量测参与度呈指数规律降低。实验结果表明,SINS/BDS紧组合导航系统能够融合SINS抗干扰能力强和BDS卫星导航准确性高的优势,该指数软卡方检验自适应EKF算法能够有效解决系统新息失配问题,提高系统鲁棒性和稳定性,具有应用价值。 展开更多
关键词 捷联式惯性导航系统 北斗卫星导航系统(BDS) 组合导航 紧组合 卡方检验 扩展卡尔曼滤波(EKF)
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GMMCC-EKF算法下新能源汽车动力锂离子电池组动静态荷电状态检测研究 认领 引用
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作者 刘敬敬 《科技资讯》 2026年第14期82-84,共3页
针对在复杂运行环境下,新能源汽车动力锂离子电池组的荷电状态估计易受非高斯噪声干扰与初始误差影响导致精度不足的问题,开展新能源汽车动力锂离子电池组动静态荷电状态检测研究。建立精确的二阶RC等效电路模型,并设计融合最大相关熵... 针对在复杂运行环境下,新能源汽车动力锂离子电池组的荷电状态估计易受非高斯噪声干扰与初始误差影响导致精度不足的问题,开展新能源汽车动力锂离子电池组动静态荷电状态检测研究。建立精确的二阶RC等效电路模型,并设计融合最大相关熵准则与噪声自适应机制的广义混合最大相关熵准则扩展卡尔曼滤波(Gaussian Mixture Model-Maximum Correntropy Criterion Extended Kalman Filter,GMMCC-EKF)算法,实现对电池组动态荷电状态(State of Charge,SOC)的精确跟踪与静态SOC的稳定标定。通过实例应用证明,该算法能够有效抑制非高斯噪声干扰,快速修正初始误差。 展开更多
关键词 GMMCC-EKF算法 动力锂离子电池组 动静态荷电状态 新能源汽车
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基于二阶AEKF算法的LiFePO4电池能量状态估计 认领 引用
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作者 刘贯科 刘洋 +3 位作者 尹照新 易斌 黄远胜 秦啸天 《电源技术》 CAS 北大核心 2026年第7期1301-1308,共8页
磷酸铁锂电池因其高安全性和长寿命被广泛应用于电动汽车等领域,但其能量状态(SOE)的准确估计仍是技术难点。因此,采用二阶自适应扩展卡尔曼滤波算法对磷酸铁锂电池的SOE进行估计。该算法结合了二阶泰勒展开与自适应机制,通过实时调整... 磷酸铁锂电池因其高安全性和长寿命被广泛应用于电动汽车等领域,但其能量状态(SOE)的准确估计仍是技术难点。因此,采用二阶自适应扩展卡尔曼滤波算法对磷酸铁锂电池的SOE进行估计。该算法结合了二阶泰勒展开与自适应机制,通过实时调整系统噪声和测量噪声的统计特性,有效应对了电池模型不确定性及工况变化带来的干扰,提高了估计精度。实验结果表明,在不同温度和充放电条件下,该算法能够准确跟踪电池SOE变化,估计误差始终保持在3%以内,显著优于传统方法。该研究为磷酸铁锂电池的精确管理和高效利用提供了有力支持,有助于提升电动汽车的续航里程预测准确性和电池系统的整体性能。 展开更多
关键词 LiFePO4电池 能量状态 二阶自适应扩展Kalman滤波算法
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基于电热耦合估计与EKF的电动汽车IGBT结温实时预测方法 认领 引用
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作者 陈斌 于津涛 +1 位作者 荐清源 陈昶健 《专用汽车》 2026年第3期46-51,61,共6页
绝缘栅双极型晶体管(IGBT)的结温是影响电动汽车驱动系统可靠性的关键参数。为解决传统估计方法精度不足的问题,提出了一种基于实时电热耦合估计与扩展卡尔曼滤波(EKF)的IGBT结温预测方法。首先,构建了一个高精度的电热耦合模型,该模型... 绝缘栅双极型晶体管(IGBT)的结温是影响电动汽车驱动系统可靠性的关键参数。为解决传统估计方法精度不足的问题,提出了一种基于实时电热耦合估计与扩展卡尔曼滤波(EKF)的IGBT结温预测方法。首先,构建了一个高精度的电热耦合模型,该模型通过精确的功率损耗计算将逆变器的实时电气运行状态转换为热源;其次,针对模型不确定性,设计了基于EKF的鲁棒结温观测器对结温状态的最优估计。基于MATLAB的仿真结果表明,与传统的Foster热网络开环估计方法相比,提出的EKF观测器将结温预测的均方根误差从1.45℃显著降低至0.31℃,平均绝对误差降低至0.21℃。 展开更多
关键词 IGBT 结温预测 电热耦合模型 扩展卡尔曼滤波 电动汽车 实时估计
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Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm 认领 引用 被引量：4
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作者 Guangbo Xu Bingting Zha +2 位作者 Hailu Yuan Zhen Zheng He Zhang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第7期1-13,共13页
The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant ... The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance. 展开更多
关键词 Laser fuze Underwater laser detection Backscatter adaptive filter Spline least mean square algorithm Nonlinear filtering algorithm
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Amplitude phase control for electro-hydraulic servo system based on normalized least-mean-square adaptive filtering algorithm 认领 引用 被引量：6
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作者 姚建均 富威 +1 位作者 胡胜海 韩俊伟 《Journal of Central South University》 SCIE EI CAS 2011年第3期755-759,共5页
The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorit... The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision. 展开更多
关键词 amplitude attenuation phase delay normalized least-mean-square adaptive filtering algorithm tracking performance electro- hydraulic servo system
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Adaptive Median Filtering Algorithm Based on Divide and Conquer and Its Application in CAPTCHA Recognition 认领 引用 被引量：4
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作者 Wentao Ma Jiaohua Qin +3 位作者 Xuyu Xiang Yun Tan Yuanjing Luo Neal NXiong 《Computers, Materials & Continua》 SCIE EI 2019年第3期665-677,共13页
As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and ... As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and improve the security of CAPTCHA.Recently,many studies have shown that improving the image preprocessing effect of the CAPTCHA,which can achieve a better recognition rate by the state-of-theart machine learning algorithms.There are many kinds of noise and distortion in the CAPTCHA images of this experiment.We propose an adaptive median filtering algorithm based on divide and conquer in this paper.Firstly,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering efficiency.Secondly,the size of the filtering window is adaptively adjusted according to the noise density.As demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median filter.The algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation details.Therefore,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications. 展开更多
关键词 Image preprocessing machine learning CAPTCHA recognition adaptive median filtering algorithm.
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Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems 认领 引用 被引量：3
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作者 YAO Yu ZHU Shanfeng CHEN Xinmeng 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1086-1090,共5页
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of consider... In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage. 展开更多
关键词 Kendall correlation collaborative filtering algorithms recommender systems positive correlation
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Improved pruning algorithm for Gaussian mixture probability hypothesis density filter 认领 引用 被引量：8
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作者 NIE Yongfang ZHANG Tao 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第2期229-235,共7页
With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved ... With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones. 展开更多
关键词 Gaussian mixture probability hypothesis density(GM-PHD) filter pruning algorithm proximity targets clutter rate
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