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Major Development Under Gaussian Filtering Since Unscented Kalman Filter 被引量:7
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作者 Abhinoy Kumar Singh 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第5期1308-1325,共18页
Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring... Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems. 展开更多
关键词 Bayesian framework cubature rule-based filtering gaussian filters gaussian sum and square-root filtering nonlinear filtering quadrature rule-based filtering unscented transformation
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An evaluation analysis method for corrosion morphology characterization based on Gaussian filter
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作者 ZHANG Peng GUO Bin CHENG Shu-kang 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2009年第S02期502-506,共5页
The corrosion process of copper in thermal flow system was investigated through the experimental bench.According to surface variation of samples during corrosion process,the surface model of specimen was build up base... The corrosion process of copper in thermal flow system was investigated through the experimental bench.According to surface variation of samples during corrosion process,the surface model of specimen was build up based on Gaussian filter.The results show that the corrosion characterization of copper in thermal flow system is pitting corrosion.The morphology characterizations of metal corrosion process can be described using the proposed surface model.The generation and development of copper pitting process can be observed clearly. 展开更多
关键词 CORROSION corrosion morphology gaussian filter COPPER
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Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach 被引量:1
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作者 W. Devapriya C. Nelson Kennedy Babu T. Srihari 《Circuits and Systems》 2016年第9期2168-2175,共8页
An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notifica... An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notification systems, Automatic road enforcement, Collision avoidance systems, Automatic parking, Map database management, etc. Advance Driver Assists System (ADAS) belongs to ITS which provides alert or warning or information to the user during driving. The proposed method uses Gaussian filtering and Median filtering to remove noise in the image. Subsequently image subtraction is achieved by subtracting Median filtered image from Gaussian filtered image. The resultant image is converted to binary image and the regions are analyzed using connected component approach. The prior work on speed bump detection is achieved using sensors which are failed to detect speed bumps that are constructed with small height and the detection rate is affected due to erroneous identification. And the smartphone and accelerometer methodologies are not perfectly suitable for real time scenario due to GPS error, network overload, real-time delay, accuracy and battery running out. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension. 展开更多
关键词 Intelligent Transportation System Speed Bumps Driver Assistance System gaussian and Median filtering Connected Component Analysis
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Effects of Gaussian filter in processing GRACE data: Gravity rate of change at Lhasa,southern Tibet 被引量:5
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作者 SUN WenKe HASEGAWA Takashi +3 位作者 ZHANG XinLin FUKUDA Yoichi SHUM C. K. WANG Lei 《Science China Earth Sciences》 SCIE EI CAS 2011年第9期1378-1385,共8页
In this paper, the spatial gravity distribution over Tibetan Plateau and the gravity rate of change at Lhasa for different Gaussian filter radii are computed using GRACE data. Results show that the estimate of the gra... In this paper, the spatial gravity distribution over Tibetan Plateau and the gravity rate of change at Lhasa for different Gaussian filter radii are computed using GRACE data. Results show that the estimate of the gravity rate of change is spatialradius-dependent of the Ganssian filter. The GRACE-estimated gravity rate of change agrees well with the surface measured one. In other words, the GRACE-estimated gravity rate of change has a limited value as that obtained by surface measurement when the spatial filter radius reaches zero. Then numerical simulations are made for different spatial radii of the Gaussian filter to investigate its behaviors when applied to surface signals. Results show that the estimate of a physical signal is filter-radius dependent. If the computing area is equal to or less than the mass area, especially for a uniformly distributed mass, the estimate gives an almost correct result, no matter what filter radius is used. The estimate has large error because of the signal leakage caused by harmonic truncation if the computing area is much bigger than the mass distribution (or inverse for a small mass anomaly). If a mass anomaly is too small, it is difficult to recover it from space observation unless the filter radius is extremely small. If the computing point (or area) is outside the mass distribution, the estimated result is almost zero, particularly for small filter radii. These properties of the Gaussian filter are helpful in applying GRACE data in different geophysical problems with different spatial position and geometrical size. We further discuss physical sources causing the scalar gravity change at Lhasa. Discussions indicate that the gravity rate of change at Lhasa is not caused by the present-day ice melting (PDIM) (or Little Ice Age, LIA) effect because no ice melting occurs in Lhasa city and nearby. The gravity rate of change is attributable mainly to tectonic deformation associated with the Indian Plate collision. Simultaneous surface displacement, surface denudation, and GIA effects are not negligible. 展开更多
关键词 gravity change GRACE gaussian filter Tibetan Plateau LHASA
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3D Profile Filter Algorithm Based on Parallel Generalized B-spline Approximating Gaussian 被引量:3
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作者 REN Zhiying GAO Chenghui SHEN Ding 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2015年第1期148-154,共7页
Currently, the approximation methods of the Gaussian filter by some other spline filters have been developed. However, thesc methods are only suitable for the study of one-dimensional filtering, when these methods are... Currently, the approximation methods of the Gaussian filter by some other spline filters have been developed. However, thesc methods are only suitable for the study of one-dimensional filtering, when these methods are used for three-dimensional filtering, it is found that a rounding error and quantization error would be passed to the next in every part. In this paper, a new and high-precision implementation approach for Gaussian filter is described, which is suitable for three-dimensional reference filtering. Based on the theory of generalized B-spline function and the variational principle, the transmission characteristics of a digital filter can be changed through the sensitivity of the parameters (t1, t2), and which can also reduce the rounding error and quantization error by the filter in a parallel form instead of the cascade form, Finally, the approximation filter of Gaussian filter is obtained. In order to verify the feasibility of the new algorithm, the reference extraction of the conventional methods are also used and compared. The experiments are conducted on the measured optical surface, and the results show that the total calculation by the new algorithm only requires 0.07 s for 480×480 data points; the amplitude deviation between the reference of the parallel form filter and the Gaussian filter is smaller; the new method is closer to the characteristic of the Gaussian filter through the analysis of three-dimensional roughness parameters, comparing with the cascade generalized B-spline approximating Gaussian. So the new algorithm is also efficient and accurate for the implementation of Gaussian filter in the application of surface roughness measurement. 展开更多
关键词 generalized B-spline gaussian filter three-dimensional reference cascade characteristic parallel characteristic
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Pattern Detection in Airborne LiDAR Data Using Laplacian of Gaussian Filter 被引量:3
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作者 ZHAN Qingming LIANG Yubin +1 位作者 CAI Ying XIAO Yinghui 《Geo-Spatial Information Science》 2011年第3期184-189,共6页
Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,it is still one of the unsolved problems in LiDAR data processing due to difficulty... Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points.The paper analyzes the characteristics of Laplacian of Gaussian(LoG) Filter and its potential use for structure detection in LiDAR data.A feature detection method based on LoG filtering is presented and ex-perimented on the unstructured points.The method filters the elevation value(namely,z coordinate value) of each point by convo-lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob-jects/features.The experiments are carried on a point cloud dataset acquired from a neighborhood area.The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size,which specifies the local area that is analyzed. 展开更多
关键词 laser scanning point cloud feature detection Laplacian of gaussian filter
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Adaptive Gaussian Noise Image Removal Algorithm Using Filtering-Based Noise Estimation 被引量:2
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作者 Tuan-anh NGUYEN Hong-son NGUYEN Min-cheol HONG 《Journal of Measurement Science and Instrumentation》 CAS 2011年第3期230-234,共5页
This paper proposes a spatially denoising algorithm using filtering-based noise estimation for an image corrupted by Gaussian noise.The proposed algorithm consists of two stages:estimation and elimination of noise den... This paper proposes a spatially denoising algorithm using filtering-based noise estimation for an image corrupted by Gaussian noise.The proposed algorithm consists of two stages:estimation and elimination of noise density.To adaptively deal with variety of the noise amount,a noisy input image is firstly filtered by a lowpass filter.Standard deviation of the noise is computed from different images between the noisy input and its filtered image.In addition,a modified Gaussian noise removal filter based on the local statistics such as local weighted mean,local weighted activity and local maximum is used to control the degree of noise suppression.Experiments show the effectiveness of the proposed algorithm. 展开更多
关键词 DENOISING local statistics gaussian filtering noise estimation gaussian noise
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Verification of Characteristics of Gaussian Filter Series for Surface Roughness in ISO and Proposal of Filter Selection Guidelines
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作者 Yuki Kondo Ichiro Yoshida +2 位作者 Dan Nakaya Munetoshi Numada Hiroyasu Koshimizu 《Nanomanufacturing and Metrology》 2021年第2期97-108,共12页
In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filt... In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filter is proposed.However,ISO16610-31:Gaussian regression filters(GRF)only provide a single method and a few examples,and does not specify the conditions under which the primary profile can be covered.Moreover,the data presented in the example on robustness in ISO16610-31 do not contain roughness components.In actual roughness measurements,no primary profile exists that does not include a roughness component.Because the characteristics of GRFs are unknown,it is not yet clear which filter should be used for which primary profile,and this is an issue that has been raised at ISO and JIS conferences.In addition,the establishment of filter selection guidelines is necessary at measurement sites.Therefore,this paper clarifies the characteristics of GF-series filters,summarizes the points to be considered when using them,and identifies the filter that should be selected according to different situations.Based on the results,a figure that visualizes the characteristics of filters and a flowchart regarding which filter should be used are created;these tools,to the best of the authors’knowledge,did not exist prior to this study.It is believed that these results will help fulfil the needs of measuring job sites and also aid in filter selection. 展开更多
关键词 Surface roughness gaussian filter Robust filter M-estimation method Fast M-estimation method
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Improved pruning algorithm for Gaussian mixture probability hypothesis density filter 被引量:7
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作者 NIE Yongfang ZHANG Tao 《Journal of Systems Engineering and Electronics》 SCIE EI 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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An Improved Gaussian Particle Filter Algorithm Using KLD-Sampling 被引量:1
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作者 ZHOU Zhaihe ZHONG Yulu +1 位作者 ZENG Qingxi TIAN Xiangrui 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2020年第4期607-614,共8页
To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algori... To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function.KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly.Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly.Compared with the particle filter algorithm using KLD-sampling(KLPF),the speed of KLGPF increases by 28%under the same conditions. 展开更多
关键词 particle filter gaussian particle filter KLD-sampling noise mutation adaptive particle numbers
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Gaussian particle filter based pose and motion estimation 被引量:1
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作者 WU Xue-dong SONG Zhi-huan 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2007年第10期1604-1613,共10页
Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fi... Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional (2D) intensity images from a single camera is desirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter (GPF) based on the panicle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF. 展开更多
关键词 gaussian particle filter (GPF) Pose and motion estimation Line features Monocular vision Extended Kalman filter(EKF) Unscented Kalman filter (UKF) Dual quatemion
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Immune adaptive Gaussian mixture particle filter for state estimation 被引量:1
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作者 Wenlong Huang Xiaodan Wang +1 位作者 Yi Wang Guohong Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第4期877-885,共9页
The particle filter (PF) is a flexible and powerful sequen- tial Monte Carlo (SMC) technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from p... The particle filter (PF) is a flexible and powerful sequen- tial Monte Carlo (SMC) technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from particle degeneracy and sample im- poverishment, which greatly affects its performance for nonlinear, non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model (IAGM) based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest obser- vation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further im- prove the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters. 展开更多
关键词 artificial immune particle filter gaussian mixturemodel.
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Marginalized cubature Kalman filtering algorithm based on linear/nonlinear mixed-Gaussian model
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作者 Hu Yumei Hu Zhentao Jin Yong 《High Technology Letters》 EI CAS 2018年第4期362-368,共7页
Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the ma... Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the marginalized technique is adopted to model the target system dynamics with nonlinear state and linear state separately,and the two parts are estimated by cubature Kalman filter and standard Kalman filter respectively. Therefore,the linear part avoids the generation and propagation process of cubature points. Accordingly,the computational complexity is reduced.Meanwhile,the accuracy of state estimation is improved by taking the difference of nonlinear state estimation as the measurement of linear state. Furthermore,the computational complexity of marginalized cubature Kalman filter is discussed by calculating the number of floating-point operation. Finally,simulation experiments and analysis show that the proposed algorithm can improve the performance of filtering precision and real-time effectively in target tracking system. 展开更多
关键词 state estimation marginalized modeling mixed-gaussian model CUBATURE KALMAN filter
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基于最优控制理论的国产光抽运小铯钟频率控制算法 被引量:1
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作者 宋会杰 董绍武 +4 位作者 王翔 姜萌 章宇 郭栋 张继海 《物理学报》 SCIE EI CAS CSCD 北大核心 2024年第6期108-118,共11页
原子钟频率控制是时间保持工作中的关键技术.当前守时工作中的频率控制主要针对国外微波钟采用开环控制算法,但由于国产光抽运小铯钟(下称国产钟)的工作原理和性能不同于国外同类型原子钟,因此该算法不能很好适应国产钟.为了提升我国标... 原子钟频率控制是时间保持工作中的关键技术.当前守时工作中的频率控制主要针对国外微波钟采用开环控制算法,但由于国产光抽运小铯钟(下称国产钟)的工作原理和性能不同于国外同类型原子钟,因此该算法不能很好适应国产钟.为了提升我国标准时间的自主性和安全性,本文基于国产钟的噪声特性,在最优控制理论的框架下研究了线性二次高斯控制算法,该算法属于闭环控制算法,从同步时间、频率控制准确度和频率控制稳定度方面研究国产钟性能,最后分析了不同控制间隔对国产钟性能的影响.结果表明随着二次损失函数中约束矩阵W_(R)的增大,同步时间延长,控制准确度降低,控制短期稳定度提高.W_(R)相同情况下,随着控制间隔的增大,同步时间延长,控制准确度降低,控制短期稳定度提高,对于W_(R)=1时,控制间隔为1 h的同步时间为5小时,控制准确度为1.83 ns,1 h的Allan偏差为1.81×10^(-13);控制间隔为8 h的同步时间为28 h,控制准确度为4.48 ns,1 h的Allan偏差为1.48×10^(-13).控制国产光抽运小铯钟的中长期稳定度都得到提高. 展开更多
关键词 原子钟状态模型 线性二次高斯控制 KALMAN滤波 原子钟噪声
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基于点云反射特性的前方道路附着系数估计方法研究
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作者 胡宏宇 唐明弘 +2 位作者 高菲 鲍明喜 高镇海 《汽车工程》 EI CSCD 北大核心 2024年第10期1842-1852,共11页
路面附着系数是影响自动驾驶系统决策控制策略的重要因素。为实现对道路附着系数前瞻性的高精度感知,本文基于车载激光雷达设计了一种新的路面附着系数估计方法。首先采集了干燥柏油路面、混凝土路面、湿滑柏油路面、结冰路面和积雪路... 路面附着系数是影响自动驾驶系统决策控制策略的重要因素。为实现对道路附着系数前瞻性的高精度感知,本文基于车载激光雷达设计了一种新的路面附着系数估计方法。首先采集了干燥柏油路面、混凝土路面、湿滑柏油路面、结冰路面和积雪路面构建道路数据集;基于使用布料模拟滤波和RANSAC算法进行了道路点云提取、基于高斯滤波去除反射率异常噪点;根据点云反射率随距离和入射角变化的规律将路面划分为不同区域分别提取特征;基于深度神经网络构建了道路识别模型,并基于采集数据集进行了训练,最后基于路面材质和峰值附着系数的统计经验确定了前方道路的附着系数。测试结果表明,本文提出的算法道路类型辨识精度超过99.3%,算法平均运行周期55ms,可实现实时高精度的路面峰值附着系数估计。 展开更多
关键词 路面附着系数 激光雷达点云 布料模拟滤波 RANSAC 深度神经网络 高斯滤波 路面类型识别
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基于渐进高斯滤波融合的多视角人体姿态估计
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作者 杨旭升 吴江宇 +1 位作者 胡佛 张文安 《自动化学报》 EI CAS CSCD 北大核心 2024年第3期607-616,共10页
针对视觉遮挡引起的人体姿态估计(Human pose estimation, HPE)性能下降问题,提出基于渐进高斯滤波(Progressive Gaussian filtering, PGF)融合的人体姿态估计方法.首先,设计分层性能评估方法对多视觉量测进行分类处理,以适应视觉遮挡... 针对视觉遮挡引起的人体姿态估计(Human pose estimation, HPE)性能下降问题,提出基于渐进高斯滤波(Progressive Gaussian filtering, PGF)融合的人体姿态估计方法.首先,设计分层性能评估方法对多视觉量测进行分类处理,以适应视觉遮挡引起的量测不确定性问题.其次,构建分布式渐进贝叶斯滤波融合框架,以及设计一种分层分类融合估计方法来提升复杂量测融合的鲁棒性和准确性.特别地,针对量测统计特性变化问题,利用局部估计间的交互信息来引导渐进量测更新,从而隐式地补偿量测不确定性.最后,仿真与实验结果表明,相比于现有的方法,所提的人体姿态估计方法具有更高的准确性和鲁棒性. 展开更多
关键词 渐进高斯滤波 自适应滤波 分布式融合 人体姿态估计
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基于肌电−惯性融合的人体运动估计:高斯滤波网络方法
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作者 杨旭升 李福祥 +1 位作者 胡佛 张文安 《自动化学报》 EI CAS CSCD 北大核心 2024年第5期991-1000,共10页
本文研究了基于肌电(Electromyography,EMG)−惯性融合的人体运动估计问题,提出了一种序贯渐进高斯滤波网络(Sequential progressive Gaussian filtering network,SPGF-net)估计方法来形成肌电和惯性的互补性优势,以提高人体运动估计精... 本文研究了基于肌电(Electromyography,EMG)−惯性融合的人体运动估计问题,提出了一种序贯渐进高斯滤波网络(Sequential progressive Gaussian filtering network,SPGF-net)估计方法来形成肌电和惯性的互补性优势,以提高人体运动估计精度和稳定性.首先,利用卷积神经网络对观测数据进行特征提取,以及利用长短期记忆(Long short-term memory,LSTM)网络模型来学习噪声统计特性和量测模型.其次,采用序贯融合的方式融合异构传感器量测特征,以建立高斯滤波与深度学习相结合的网络模型来实现人体运动估计.特别地,引入渐进量测更新对网络量测特征的不确定性进行补偿.最后,通过实验结果表明,相比于现有的卡尔曼滤波网络,该融合方法在上肢关节角度估计中的均方根误差(Root mean square error,RMSE)下降了13.8%,相关系数(R^(2))提高了4.36%. 展开更多
关键词 高斯滤波网络 多传感器融合 人体运动估计 非线性卡尔曼滤波
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基于嵌入式设备的低功耗工业读码器系统设计
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作者 何涛 陈文重 +1 位作者 王正家 吴春林 《电子器件》 CAS 2024年第1期55-61,共7页
针对目前国内读码器产品种类稀少、成本过高,功耗和热量过高、识别率低等问题,提出了一种结构紧凑、性价比高、功耗低、稳定性强的智能读码器系统设计方案。该系统方案主要基于ARM平台和Linux系统开发,硬件方面采用NXP MX8Mini作为主控... 针对目前国内读码器产品种类稀少、成本过高,功耗和热量过高、识别率低等问题,提出了一种结构紧凑、性价比高、功耗低、稳定性强的智能读码器系统设计方案。该系统方案主要基于ARM平台和Linux系统开发,硬件方面采用NXP MX8Mini作为主控芯片,搭载XILINX的XC7A50T FPGA器件,配置2G容量的LDDR4内存用来做图像预处理工作,采用AR0144CS芯片作为图像传感器,并支持多种通讯协议和扩展。软件方面,主要介绍了系统FPGA图像预处理及高斯滤波具体实现、ARM与FPGA的通讯、上位机功能设计。经过测试,该读码器系统运行稳定、体积小、功耗低,一维码8mil Code128识别率达到99.99%,为当前国产读码器市场提供了一种新的解决方案。 展开更多
关键词 读码器 低功耗 识别率 图像预处理 高斯滤波
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机器学习的高精度毫米波雷达测距信号误差补偿方法
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作者 李淑玲 姚香秀 张俊丽 《激光杂志》 CAS 北大核心 2024年第8期224-229,共6页
毫米波雷达是一种常用的非接触式测距技术,受环境因素以及测量过程中存在的各种误差影响,测距结果可能存在一定的误差。研究误差补偿方法可以有效提高毫米波雷达的测距精度,从而更准确地获取目标物体的距离信息。为此,提出了机器学习的... 毫米波雷达是一种常用的非接触式测距技术,受环境因素以及测量过程中存在的各种误差影响,测距结果可能存在一定的误差。研究误差补偿方法可以有效提高毫米波雷达的测距精度,从而更准确地获取目标物体的距离信息。为此,提出了机器学习的高精度毫米波雷达测距信号误差补偿方法。通过高斯滤波器去除雷达测距信号中的噪声,完成信号的去噪处理。利用模拟插入脉冲计数法和四象限光斑定位法,测量目标物体的距离和角度信息,通过自适应惯性权重与收敛因子优化粒子群算法,并利用优化后的粒子群算法改进BP神经网络,将测量的距离和角度信息输入到改进的BP神经网络中展开训练,即可得到补偿后的雷达测距信号。实验结果表明,该方法的信号处理效果好,补偿后的毫米波雷达测距信号方位角和俯仰角误差接近于0,且信号平滑度较高。 展开更多
关键词 机器学习 毫米波雷达 误差补偿 高斯滤波器 BP神经网络
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Interactive Kalman Filtering for Differential and Gaussian Frequency Shift Keying Modulation with Application in Bluetooth 被引量:3
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作者 Mahdi N. Ali Mohamed A. Zohdy 《Journal of Signal and Information Processing》 2012年第1期63-76,共14页
Some applications are constrained only to implement low cost receivers. In this case, designers are required to use less complex and non-expensive modulation techniques. Differential Quadrature Phase Shift Keying (DQP... Some applications are constrained only to implement low cost receivers. In this case, designers are required to use less complex and non-expensive modulation techniques. Differential Quadrature Phase Shift Keying (DQPSK) and Gaussian Frequency Shift Keying (GFSK) can be non-coherently demodulated with simple algorithms. However, these types of demodulation are not robust and suffer from poor performance. This paper proposes a new method to enhance the performance of DQPSK and GFSK using Interactive Kalman Filtering (IKF) technique, in which a one Unscented Kalman Filter (UKF) and two Kalman Filters (KF) are coupled to optimize the demodulated signals. This method consists of simple but very effective algorithms without adding complexity to the demodulators comparing to other very complex methods. UKF is used in this method due to its superiority in approximating and estimating nonlinear systems and its ability to handle non-Gaussian noise environments. The proposed method has been validated by creating a MATLAB/SIMULINK Bluetooth system model, in which the IKF is integrated into the receiver, which implement both DQPSK and GFSK, and run simulation in Gaussian and Non-Gaussian noise environments. Results have shown the effectiveness of this method in optimizing the received signals, and that the UKF outperforms the Extended Kalman Filter (EKF). 展开更多
关键词 INTERACTIVE KALMAN filtering Unscented KALMAN filter Extended KALMAN filter DIFFERENTIAL Quadrature Phase SHIFT Keying gaussian Frequency SHIFT Keying BLUETOOTH
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