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53rd International Conference on Parallel Processing (ICPP)

作者： Xiao, Pingdan Hong, Qinghui Du, Sichun Zhang, Jiliang Hunan Univ Changsha Peoples R China

ISBN: (纸本)9798400717932

kalman filter (KF) algorithm, which can solve the state estimation problem of multi-variable and complex dynamical system, plays a pivotal role in a multitude of engineering scenarios. However, the traditional digital computing architecture represented by the vonNeumann architecture are currently confronted with high overhead challenges in terms of latency, energy, and area when executing KF algorithm. Aiming at the problem, we propose CIM-KF, the first Computing-in-memory (CIM) circuits for KF algorithm. CIMKF can efficiently carry out the entire process of KF algorithm by capitalizing on the large-scale parallel computation inherent in the CIM architecture. The evaluation shows that CIM-KF has average 96.15% accuracy for 32-th order parameter matrix in KF algorithm, and can be 13.89x similar to 55.21x faster than existing ASIC and FPGA implementations of KF algorithm. The results also demonstrate that CIM-KF can deliver up to 3.52x energy efficiency improvement and 54.79x area efficiency improvement, and reduce over 90% latency overhead, compared with the SOTA counterparts. Furthermore, we propose a novel design of ReRAM-CIM architecture, underpinned by the CIM-KF, aimed at precise and rapid calculation of the state of capacity and terminal voltage in Battery Management Systems. By evaluation, our proposed architecture boasts the estimation accuracy within 2% error for the individual battery cell of state of capacity and terminal voltage. With the strengths of the parallel computing intrinsic to CIM architecture and the speed of analog circuit, our architecture facilitates speed up to 70.6x in accurate estimation when bench-marked against the SOTA work.

关键词： Computing-in-memory ReRAM Circuit design kalman filter algorithm

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Dynamic self-calibration of time grating sensors based on self-adaptive kalman filter algorithm

Dynamic self-calibration of time grating sensors based on se...

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6th International Symposium on Precision Mechanical Measurements (ISPMM)

作者： Chen Ziran Chongqing Univ Technol Chongqing Key Lab Time Grating Sensing & Adv Test Minist Educ Engn Res Ctr Mech Testing Technol & Equipment Chongqing Peoples R China

ISBN: (纸本)9780819497925

As regarding the errors caused by mechanical installation and signal processing, a novel self-adaptive kalman filter algorithm is proposed. According to kalman filter recursion formula, the current value can be estimated with the last estimated value and the current measured value. Therefore, the kalman filter algorithm does not need recorder the past series of the estimated value and the current measured value, the calculated load is not too heavy to implement. The respond speed and the quality of time grating can be improved significantly. Experiment results prove the valid of the proposed method.

关键词： kalman filter algorithm time grating sensor self- adaptive

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On-line Monitoring Method of Submodule in MMC Based on kalman filter algorithm

On-line Monitoring Method of Submodule in MMC Based on Kalma...

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2020 Asia Energy and Electrical Engineering Symposium

作者： Li, Yixin Zheng, Wendi Xu, Qidong Fuzhou Univ Sch Elect Engn & Automat Fuzhou Peoples R China

ISBN: (纸本)9781728167824

In flexible high voltage direct current (HVDC) system, aging and failure of the submodule components in modular multilevel converter (MMC) threatens the safe and stable operation of the system. In this paper, a method on monitoring parameters of capacitors, IGBTs and diodes in submodule (SM) simultaneously is proposed, which presents the state of SMs. According to the relationship between the state of switching devices and the voltage of capacitor in SMs, a mathematical model of bridge arm voltage in MMC is established. Then, kalman filter is introduced to monitor the parameters of elements, which can prevent the influence of sensor measurement noise and save storage space. Next, the state of the submodule capacitor, IGBTs and the diodes is monitored through the established mathematical model and repeating the kalman filter algorithm multiple times. Finally, the scheme is verified by building an MMC-HVDC transmission system in Power Systems Computer Aided Design (PSCAD) simulation platform. The result shows that the estimation error is less than 1%, which is satisfied to the online monitoring in practical engineering and can improve the reliability of the system.

关键词： Modular multilevel Converter ( MMC) submodule (SM) kalman filter algorithm state parameter estimation

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Adaptive filtering of color noise using the kalman filter algorithm

Adaptive filtering of color noise using the Kalman filter al...

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17th IEEE Instrumentation and Measurement Technology Conference

作者： Xiong, SS Zhou, ZY Zhong, LM Xu, CS Zhang, WD Tsing Hua Univ Dept Precis Instruments & Mechanol Beijing 100084 Peoples R China

ISBN: (纸本)0780358902

In the view of the drawbacks of the conventional kalman filtering, a kind of adaptive kalman filtering approach of color noise based on the correlative method of the system output is proposed. This approach builds the kalman filtering model of color noise by adopting the equivalent measurement equation in order to avoid complicated computation and the expansion of the dimension of the filter. It is unnecessary to know the variance of the measurement noise beforehand so that it is closer to the actual situation. The correlative method of the system output is presented to estimate the variance of the measurement noise. The adaptive filtering of color noise using the kalman filter algorithm is applied to solve the cephalometric images of stomatology. Several experimental results demonstrate the feasibility and good performance of the approach.

关键词： image processing medical image processing random noise interference suppression adaptive kalman filters correlation methods diagnostic radiography color noise kalman filter algorithm adaptive kalman filtering correlative method equivalent measurement equation measurement noise adaptive filtering cephalometric images stomatology X-ray images

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Groundwater pollution source identification using Metropolis-Hasting algorithm combined with kalman filter algorithm

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JOURNAL OF HYDROLOGY 2023年第PartA期626卷

作者： Luo, Jiannan Li, Xueli Xiong, Yu Liu, Yong Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun 130021 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130021 Peoples R China

Increasing the precision of groundwater pollution source identification (GPSI) is crucial for groundwater pollution control and risk management. Bayesian theory based on the Markov Chain Monte Carlo (MCMC) method is a useful strategy of solving the GPSI problem. However, because of the nonlinear and uncertainty characteristics of GPSI, the Metropolis-Hasting (MH) algorithm, one of the most well-known MCMC algorithms, has the disadvantage of relatively low precision and is time-consuming. To address this problem, the kalman filter (KF) algorithm was combined with the MH algorithm and referred to as the kalman filter MetropolisHasting (KF-MH) algorithm. The algorithm generates a new initial distribution that is close to the true value through a prior distribution, and the new initial distribution is used to perform subsequent iterations of the calculation. The viability and superiority of the proposed KF-MH algorithm were assessed in three hypothetical GPSI cases under different conditions. In the inversion process, a surrogate model was constructed using a temporal convolutional network (TCN) to reduce the computational pressure imposed by the numerical simulation model. The results of the TCN surrogate model in the cases illustrate the high accuracy of the TCN surrogate model in fitting the groundwater numerical model. In the three cases, the normalized errors between the identification results and the true values of the source features obtained with the KF-MH algorithm were significantly lower than those of the MH algorithm. The results indicate that the proposed KF-MH algorithm has higher inversion accuracy than the MH algorithm.

关键词： Groundwater pollution source Temporal convolutional network Surrogate model Metropolis-Hasting algorithm kalman filter algorithm

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Application of the Improved kalman filter algorithm to the Servo Tracking System

Application of the Improved Kalman Filter Algorithm to the S...

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2011 International Conference on Quality,Reliability,Risk,Maintenance,and Safety Engineering(2011年质量、可靠性、风险、维修性与安全性国际会议暨第二届维修工程国际学术会议 ICQR2MSE 2011)

作者： Jiansheng YAN Yingwei JIN Shu AN Electrical Engineering of School MechanicaEngineering of Shijiazhuang Shijiazhuang china

In the boat satellite TV receiving system, the quartz gyro sensor is used to sense the rotation rate of all dimensions, and provide parameters for servo of posture. According to requirements of the system performance index, after analyzing the range of each direction’s angular speed variation of the carrier and considering cost performance, in this task we use quartz gyro of *** velocity information measured by gyro scope can become angle information by integral transformation and become angular acceleration information by differential transformation. The noise in signal which is exported by quartz gyro sensor is very obvious even the quartz gyro sensor is at a stand still. If the signal is used to track directly or after simple filtering, tracking performance is poor even cannot tracking. Zero wander of random drift in gyro is anon-stationary random process. Before selecting appropriate filtering method we need modeling for gyro noise signal. The noise out put from gyro can be described by AR model in a sufficiently large order. According to the servo tracking system requirements, kalman filter algorithm is used to forecast the target's position and speed. Consider processing tracking signal in case of the target is blocked, we improve kalman filter algorithm. The system obtains good effecting exactly forecasting the position of target's image and the smooth of target's speed. In practical application, the system in high speed and high precision realized steady tracking. It can reach tactical and technical requirements of tracking system.

关键词： kalman filter algorithm quartz gyro servo tracking system

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Characteristics of a real-time ensemble time scale corrected by the KRISS-PSFS using a reduced kalman filter (Kred) algorithm

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METROLOGIA 2023年第4期60卷 045006-045006页

作者： Lee, Ho Seong Kwon, Taeg Yong Park, Sang Eon Lee, Young Kyu Yang, Sung-hoon Yu, Dai-Hyuk Heo, Myoung-Sun Kim, Huidong Park, Chang Yong Lee, Won-Kyu Korea Res Inst Stand & Sci Daejeon 34113 South Korea

An ensemble time, which is indicated by TA (temps atomique, in French), was generated every minute by combining four active hydrogen masers with the reduced kalman filter (Kred) algorithm. The TA frequency was averaged over 12 h and then the mean frequency was steered to the primary and secondary frequency standards developed by KRISS with a micro-phase stepper (MPS). The steered and physically realized TA frequency is called KRISS ensemble time (KET). We investigated the frequency-state estimate outputted from the Kred algorithm and the control signal applied to the MPS, according to the abnormal temperature change of the room where the hydrogen masers were placed. When the frequency of the TA was corrected by the KRISS-F1, the Allan deviation of KET was 1.72 x 10(-15) at the average time & tau;= 1 d, and the variation of [UTC-KET] was stable within 2 ns for 75 d.

关键词： ensemble time scale hydrogen masers frequency steering kalman filter algorithm

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Optimization of Parallel algorithm for kalman filter on CPU-GPU Heterogeneous System 12

Optimization of Parallel Algorithm for Kalman Filter on CPU-...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Xu, Dandan Xiao, Zheng Li, Dapu Wu, Fan Hunan Univ Coll Comp Sci & Elect Engn Changsha 410008 Hunan Peoples R China

ISBN: (纸本)9781509040933

For linear dynamic systems in data processing, kalman filter (KF) algorithm can produce more precise estimations of unknown variables and analyze complex state transition. For a complex application, the large scale of system states and the less runtime of KF algorithm are necessarily needed. Therefore, more available computing resources are crucial to accelerate computation. This paper presents an optimization method to accelerate the computation of KF using modern graphics processing unit (GPU), and enlarge the scale of the estimations for linear dynamical systems. In addition, some evaluations and tests, which are obtained on different parallel architectures with different number of estimation size, are discussed. The experimental results demonstrate that GPU-based computations are typically faster than the corresponding sequential execution for different datasets, and the maximum speedup is 50x in the test environment. Furthermore, it shows a scalable speedup by GPU with enlarging the scale of estimate size within the GPU memory limitation.

关键词： CPU-GPU heterogeneous system Compute Unified Device Architecture hybrid computing kalman filter algorithm state estimation

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Mean-Shift Tracking algorithm based on kalman filter using Adaptive Window and Sub-blocking 11

Mean-Shift Tracking Algorithm based on Kalman Filter using A...

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11th World Congress on Intelligent Control and Automation

作者： Wang, Xingmei Hu, Zhihao Feng, Jingjiao Li, Lin Harbin Engn Univ Comp Sci & Technol Harbin Heilongjiang Pr Peoples R China Harbin Engn Univ Informat & Commun Engn Harbin Heilongjiang Pr Peoples R China

ISBN: (纸本)9781479958252

To reduce the tracking errors caused by highspeed motion and variable motion in the process of moving target tracking, a novel Mean-Shift tracking algorithm based on kalman filter using adaptive window and sub-blocking is proposed in this paper. Moving target's utmost position is predicted by combining kalman filter and historical information, which is used as the initial position. During describing feature model of target and candidate regions, they are blocked and each sub-block region is processed by reducing RGB interval, by which computational efficiency will be improved. Finally, window bandwidth will be enlarged and reduced according to Bhattacharyya coefficient, and it achieves accurate moving target tracking by adaptive window. The comparison experiments of the Coastguard standard image sequence and car image sequence demonstrate that the proposed tracking algorithm is insensitive to high-speed motion and variable motion of moving target, and it has better tracking performance.

关键词： Moving target tracking Mean-Shift algorithm kalman filter algorithm Sub-blocking

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kalman filter spot centroid detection algorithm in optical wireless communication

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OPTICAL ENGINEERING 2022年第5期61卷 056104-056104页

作者： Yang, Shangjun Ke, Xizheng Sun, Dengke Liang, Jingyuan Zhang, Yuhao Xian Univ Technol Fac Automat & Informat Engn Xian Peoples R China Shaanxi Civil Mil Integrat Key Lab Intelligence Xian Peoples R China Shaanxi Univ Technol Coll Phys & Elect Hanzhong Peoples R China

Atmospheric turbulence creates a light spot on the focal plane of the receiving end scintillation and angle of arrival fluctuation in optical wireless communication. The combination of a spot centroid detection algorithm and kalman filter (KF) is proposed for spot tracking. The theoretical calculated value of the spot circle fit is filtered to obtain an optimal estimate of the spot center. The root mean square error of the detected spot centroids is used as an evaluation metric to verify the conventional algorithm and KF algorithm for the real-time tracking of the laser spot at transmission distances of 1.3, 4.1, and 10.3 km. The results show that the root mean square error of the spot in the pitch direction decreases from 0.740, 2.537, and 7.256 pixels to 0.683, 1.792, and 4.756 pixels, respectively, whereas the root mean square error of the spot in the horizontal direction decreases from 0.481, 2.131, and 5.932 pixels to 0.463, 1.471, and 4.362 pixels, respectively, compared with the conventional algorithm at three transmission distances. The envelope fluctuation of the baseband signal at the receiving end is significantly improved using KF algorithm compared with the conventional algorithm, which improves the stability of the optical wireless communication link. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： optical wireless communication atmospheric turbulence spot detection kalman filter algorithm

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