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adaptive kalman filter for MEMS IMU data fusion using enhanced covariance scaling

Control Theory and Technology 2021年第3期19卷 365-374页

作者： Fuseini Mumuni Alhassan Mumuni Electrical and Electronic Engineering Department University of Mines and Technology(UMaT)P.O.Box 237TarkwaGhana Electrical and Electronics Engineering Department Cape Coast Technical University(CCTU)P.O.Box DL 50Cape CoastGhana

MEMS(micro-electro-mechanical-system)IMU(inertial measurement unit)sensors are characteristically noisy and this presents a serious problem to their effective use.The kalman filter assumes zero-mean Gaussian process and measurement noise variables,and then recursively computes optimal state estimates.However,establishing the exact noise statistics is a non-trivial task.Additionally,this noise often varies widely in operation.Addressing this challenge is the focus of adaptive kalman filtering techniques.In the covariance scaling method,the process and measurement noise covariance matrices Q and R are uniformly scaled by a scalar-quantity attenuating window.This study proposes a new approach where individual elements of Q and R are scaled element-wise to ensure more granular adaptation of noise components and hence improve accuracy.In addition,the scaling is performed over a smoothly decreasing window to balance aggressiveness of response and stability in steady state.Experimental results show that the root mean square errors for both pith and roll axes are significantly reduced compared to the conventional noise adaptation method,albeit at a slightly higher computational cost.Specifically,the root mean square pitch errors are 1.1∘under acceleration and 2.1∘under rotation,which are significantly less than the corresponding errors of the adaptive complementary filter and conventional covariance scaling-based adaptive kalman filter tested under the same conditions.

关键词： IMU state-space model Role and pitch estimation More Attitude estimation adaptive kalman filter Covariance scaling

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adaptive kalman filter and dynamic recurrent neural networks-based control design of macro-micro manipulator

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控制理论与应用（英文版） 2012年第4期10卷 504-510页

作者： Lijun ZHANG Lixin YANG Lining SUN Xingwen ZHANG Department of Automation Harbin Engineering University School of Marine Engineering Northwestern Polytechnical University College of Electrical & Information Engineering Heilongjiang Institute of Science & Technology Robotics Institute Harbin Institute of Technology College of Aeronautics Science and Technology Beijing University of Aeronautics and Astronautics

In this paper, a composite control scheme for macro-micro dual-drive positioning stage with high accel- eration and high precision is proposed. The objective of control is to improve the precision by reducing the influence of system vibration and external noise. The positioning stage is composed of voice coil motor （VCM） as macro driver and piezoelectric actuator （PEA） as micro driver. The precision of the macro drive positioning stage is improved by the com- bined PID control with adaptive kalman filter （AKF）. AKF is used to compensate VCM vibration （as the virtual noise） and the external noise. The control scheme of the micro drive positioning stage is presented as the integrated one with PID and intelligent adaptive inverse control approach to compensate the positioning error caused by macro drive positioning stage. A dynamic recurrent neural networks （DRNN） based inverse control approach is proposed to offset the hysteresis nonlinearity of PEA. Simulations show the positioning precision of macro-micro dual-drive stage is clearly improved via the proposed control scheme.

关键词： Macro-micro dual-drive positioning stage Piezoelectric actuator adaptive kalman filter Dynamic re-current neural networks

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adaptive kalman filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft

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Journal of Beijing Institute of Technology 2008年第4期17卷 434-438页

作者：周峰孟秀云 School of Aerospace Science and Engineering Beijing Institute of Technology

The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive kalman filter is better than the conventional kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of kalman filter using attitude match method.

关键词： transfer alignment adaptive kalman filter wing flexure of the aircraft velocity and attitudematch method

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IAE-adaptive kalman filter for INS/GPS integrated navigation system

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Journal of Systems Engineering and Electronics 2006年第3期17卷 502-508页

作者： Bian Hongwei Jin Zhihua Tian Weifeng Dept. of Information Measurement Technology and Instruments Shanghai Jiaotong Univ. Shanghai 200030 P. R. China Dept. of Electrical Engineering of Naval Univ. of Engineering Wuhan 430033

A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the kalman filter.

关键词： inertial navigation system global positioning system integrated navigation system adaptive kalman filter

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Improved innovation-based adaptive kalman filter for dual-frequency navigation using carrier phase

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Science China(Information Sciences) 2010年第12期53卷 2653-2663页

作者： LU ChenXi,TAN YunHua ,ZHU BoCheng & ZHOU LeZhu School of Electronics Engineering and Computer Science,Peking University,Beijing 100871,China School of Electronics Engineering and Computer Science Peking University Beijing China

A new adaptive kalman filtering algorithm for dual-frequency navigation using carrier phase is presented.This algorithm gives consideration to both robustness and sensitivity by combining the two main approaches of adaptive kalman filtering based on innovation-based adaptive estimation(IAE) and fading filtering.Applicable to the navigation using carrier phase measurements typically,this algorithm can balance the weights of observations and predicted states preferably.Applications to both practical static data and kinetic simulations are presented to demonstrate the validity and efficiency of the algorithm.

关键词： navigation adaptive kalman filter carrier phase

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GPS Navigation Using adaptive kalman filter for Maneuvering Vehicle

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Computer Aided Drafting,Design and Manufacturing 2008年第1期18卷 83-87页

作者： MOATASEM Momtaz QASIM Zeeshan School of Astronautics Beijing University of Aeronautics and Astronautics

Several filter techniques were available for the GPS position estimation problem of maneuvering vehicle ranging from using different process noises to Interactive Multiple Model (IMM). The limitation of using standard kalman filters is listed.The performance of proposed adaptive filter is compared with that of the standard ones,two types of dynamic modeling of the maneuvering vehicle are used.The simulation is based on the almanac data of the GPS satellites to compute its feasibility during the simulation time and position on shape 8 track with continuous vehicle maneuvering. The goal is to obtain computationally efficient filter with reasonable accuracy for vehicle in maneuvering situation. The filter proposed is an alternative to the filter proposed in Ref. [1] with low computational burden.

关键词： GPS maneuvering fling kalman filter adaptive kalman filter

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adaptive Two-Stage kalman filter in the Presence of Random Bias

Adaptive Two-Stage Kalman Filter in the Presence of Random B...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： HU Yi-ming College of Automation of Northwestern Polytechnical University NWPU XI’AN China Qin Yong-yuan College of Automation of Northwestern Polytechnical University NWPU XI’AN China

The two-stage kalman filter requires the accurate information of unknown random bias.Unfortunately,this algebraic constraint is seldom satisfied for practical systems.The adaptive solution of estimating a set of dynamic state in the presence of a random bias employing a two-stage kalman estimator is addressed.The solution performed well when the information of unknown random bias was inaccurate.The validity of the solution is verified with a simulative example.

关键词： Two-stage kalman estimation random bias adaptive kalman filter

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An Easily Compatible Eye-tracking System for Freely-moving Small Animals

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Neuroscience Bulletin 2022年第6期38卷 661-676页

作者： Kang Huang Qin Yang Yaning Han Yulin Zhang Zhiyi Wang Liping Wang Pengfei Wei Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science Guang-dong Provincial Key Laboratory of Brain Connectome and BehaviorCAS Center for Excellence in Brain Science and Intelligence TechnologyBrain Cognition and Brain Disease InstituteShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research InstitutionsShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Seiences Bejing 100049China Harbin Institute of Technology Shenzhen Shenzhen 518055.China

Measuring eye movement is a fundamental approach in cognitive science as it provides a variety of insightful parameters that reflect brain states such as visual attention and emotions.Combining eye-tracking with multimodal neural recordings or manipulation techniques is beneficial for understanding the neural substrates of cognitive function.Many commercially-available and custom-built systems have been widely applied to awake,head-fixed small animals.However,the existing eyetracking systems used in freely-moving animals are still limited in terms of their compatibility with other devices and of the algorithm used to detect eye movements.Here,we report a novel system that integrates a general-purpose,easily compatible eye-tracking hardware with a robust eye feature-detection algorithm.With ultra-light hardware and a detachable design,the system allows for more implants to be added to the animal's exposed head and has a precise synchronization module to coordinate with other neural implants.Moreover,we systematically compared the performance of existing commonly-used pupil-detection approaches,and demonstrated that the proposed adaptive pupil feature-detection algorithm allows the analysis of more complex and dynamic eye-tracking data in freemoving animals.Synchronized eye-tracking and electroencephalogram recordings,as well as algorithm validation under five noise conditions,suggested that our system is flexibly adaptable and can be combined with a wide range of neural manipulation and recording technologies.

关键词： Eye-tracking Freely-moving Head-mounted device Pupil detection adaptive kalman filter

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Fuzzy adaptive kalman filter Algorithm for RUAV's Integrated Navigation System

Fuzzy Adaptive Kalman Filter Algorithm for RUAV's Integrated...

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第24届中国控制与决策会议

作者： Lei Dai 1,2 , Chong Wu 1,2 , Juntong Qi 1 , Janda Han 1 1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016 China 2. Graduate School of the Chinese Academy of Sciences, Beijing 100049 China

The kalman filter has characteristics of the noise-sensitive. This paper analyzes the adaptive kalman filter algorithms which are based on Sage-Husae, neural network and fuzzy logic method. And an adaptive kalman filter based on fuzzy logic is designed to estimate the attitude, heading and velocity of the RUAV. Combining the characteristics of RUAV platform and analyzing the real flight data, the fuzzy inference rules are designed to change the filtering parameters. With the actual flight data, the simulation verifies the validity of this algorithm. The experiments prove that this method can improve the navigation precision of RUAV.

关键词： Integrated Navigation adaptive kalman filter Fuzzy Logic

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Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

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Chinese Journal of Mechanical Engineering 2022年第6期35卷 351-362页

作者： Ning Zhang Yuan Yang Jianhua Wu Ziqian Zhao Guodong Yin School of Mechanical Engineering Southeast UniversityNanjing 211189China School of Instrument Science and Engineering Southeast UniversityNanjing 210096China

Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts;off-road condition,obstacles and extreme operating condition could still trigger NVH problems unexpectedly.This paper proposes a vehicular electronic image stabilization(EIS)system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles.Firstly,feature point detection and matching based on an oriented FAST and rotated BRIEF(ORB)algorithm are implemented to match images in the process of EIS.Furthermore,a novel improved random sampling consensus algorithm(i-RANSAC)is proposed to eliminate mismatched feature points and increase the matching accuracy significantly.And an adaptive kalman filter(AKF)is applied to improve the adaptability of the vehicular EIS.Finally,an experimental platform based on a gasoline model car was established to validate its performance.The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.

关键词： Electronic image stabilization Environment perceptive function Feature point adaptive kalman filter Gasoline model car

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