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

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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 *** kalman filter assumes zero-mean Gaussian process and measurement noise variables,and then recursively computes optimal state ***,establishing the exact noise statistics is a non-trivial ***,this noise often varies widely in *** this challenge is the focus of adaptive kalman filtering *** the covariance scaling method,the process and measurement noise covariance matrices Q and R are uniformly scaled by a scalar-quantity attenuating *** 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 *** addition,the scaling is performed over a smoothly decreasing window to balance aggressiveness of response and stability in steady *** 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 ***,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

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

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

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

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 1. School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

A new adaptive kalman filtering algorithm for dual-frequency navigation using carrier phase is *** 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 *** to the navigation using carrier phase measurements typically,this algorithm can balance the weights of observations and predicted states *** 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

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 *** performance of proposed adaptive filter is compared with that of the standard ones,two types of dynamic modeling of the maneuvering vehicle are *** 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

Application of adaptive kalman filter in rocket impact point estimation

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Journal of Measurement Science and Instrumentation 2015年第3期6卷 212-217页

作者：闫小龙陈国光白敦卓中北大学机电工程学院山西太原030051 豫西工业集团有限公司河南南阳473000

In order to measure the parameters of flight rocket by using radar,rocket impact point was estimated accurately for rocket trajectory *** kalman filter with adaptive filter gain matrix was *** to the particle trajectory model,the adaptive kalman filter trajectory model was constructed for removing and filtering the outliers of the parameters during a section of flight detected by three-dimensional data radar and the rocket impact point was *** results of numerical simulation show that the outliers and noise in trajectory measurement signal can be removed effectively by using the adaptive kalman filter and the filter variance can converge in a short period of *** on the relation of filtering time and impact point estimation error,choosing the filtering time of 8-10 scan get the minimum estimation error of impact point.

关键词： rocket adaptive kalman filter outliers impact point estimation

Design of Transfer Alignment Optimization Algorithm Based on Modified adaptive kalman filter

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Design of Transfer Alignment Optimization Algorithm Based on...

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

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作者： ZHANG Wei WU Zhen-hua NIE Rui Chinese Flight Test Establishment

The transfer alignment performance of ship strapdown inertial navigation is easily affected by the system error model and filtering algorithm. This paper firstly proposes an integrated error model of dynamic deflection and lever effect in which the rigid lever acceleration is compensated, and the random process error model is extended to the filter state variables for preferable estimation. Secondly, a simplified Sage-Husa adaptive kalman filter algorithm is modified through decomposing the state estimation covariance matrix by UD. On the premise of ensuring the semi-positive quality of the noise estimator, the calculation error during filtering iteration is reduced and the numerical stability is further improved. In addition, the divergent filtering suppression method based on covariance matching is introduced to improve the timeliness of filtering results. Simulation results show that when the prior statistical characteristics of system noise are difficult to accurately determine, the modified adaptive filtering algorithm can maintain the numerical stability and realize the adaptive estimation of the noise. The installation misalignment angle can be estimated more quickly and effectively,and the fast transfer alignment of SINS can be achieved.

关键词： transfer alignment adaptive kalman filter UD decomposition covariance matching

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