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Widely linear UKF constant modulus algorithm for blind adaptive beamforming

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Journal of Systems Engineering and Electronics 2017年第3期28卷 413-423页

作者： Huaming Qian Ke Liu Long Li Linchen Qian Junda Ma College of Automation Harbin Engineering University Advanced Signal Processing Lab College of EngineeringTemple University Information Communication Branch State Grid Heilongjiang Electric Power Company Limited School of Computing Beihang University

Based on a uniform linear array, a new widely linear unscented kalman filter-based constant modulus algorithm (WL-UKF-CMA) for blind adaptive beamforming is proposed. The new algorithm is designed according to the constant modulus criterion and takes full advantage of the noncircular property of the signal of interest (SOI), significantly increasing the output signal-to interference-plus-noise ratio (SINR), enhancing the convergence speed and decreasing the steady-state misadjustment. Since it requires no known training data, the proposed algorithm saves a large amount of the available spectrum. Theoretical analysis and simulation results are presented to demonstrate its superiority over the conventional linear least mean square-based CMA (L-LMS-CMA), the conventional linear recursive least square-based CMA (L-RLS-CMA), WL-LMS-CMA, WL-RLS-CMA and L-UKF-CMA.

关键词： widely linear filtering blind beamforming noncircular signals constant modulus algorithm unscented kalman filtering

UKF-based attitude determination method for gyroless satellite

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Journal of Systems Engineering and Electronics 2004年第2期15卷 105-109页

作者： ZhangHongmei DengZhenglong DepartmentofControlScienceandEngineering HarbinInstituteofTechnologyHarbin150001P.R.China

UKF (unscented kalman filtering) is a new filtering method suitable to nonlinear systems. The method need not linearize nonlinear systems at the prediction stage of filtering, which is indispensable in EKF ( extended kalman filtering) . As a result, the linearization error is avoided, and the filtering accuracy is greatly improved. UKF is applied to the attitude determination for gyroless satellite. Simulations are made to compare the new filter with the traditional EKF. The results indicate that under same conditions, compared with EKF, UKF has faster convergence speed, higher filtering accuracy and more stable estimation performance.

关键词： unscented kalman filtering attitude estimation gyroless nonlinear filtering.

The geomagnetic filtering algorithm based on correlative probability density add-weight

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The geomagnetic filtering algorithm based on correlative pro...

第九届中国卫星导航学术年会

作者： Yang Chong Hongzhou Chai Wei Liu Yulong Kong Zongpeng Pan Jie Chen Information Engineering University State Key Laboratory of Geo-information Engineering Department of Navigation Naval University of Engineering

To overcome the linearizated errors from the state equation and observation equation based on Extended kalman Filter(EKF),a unscented kalman Filter(UKF)matching algorithm using geomagnetic anomaly based on probability weighted was *** the problem that the quasi observations might be arose by choosing the same weight coefficient as the UT transformation,the geomagnetic anomaly UKF filtering algorithm associated with probability density function to assign weight for the sampled observation has been *** two experiments have been carried out in the South China Sea from the Earth Magnetic Anomaly Grid2(EMAG2),it is shown that the problem mentioned above could be overcome based on probability weighted,the drifting errors of inertial navigation system in longitude and latitude can be reduced by the modified algorithm,and the positioning accuracy and reliability of the modified algorithm is obviously superior to that of the traditional UKF algorithm and the Inertial Navigation System(INS).

关键词： Correlative Probability Density Geomagnetic aided inertial navigation unscented kalman filtering Add-Weight Nonlinear

Dynamic Toolface Measurement for Dynamic Directional Rotary Steerable Drilling Based on a Fusion filtering Algorithm

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Dynamic Toolface Measurement for Dynamic Directional Rotary ...

第32届中国过程控制会议(CPCC2021)

作者： Peng Wang Shurong Li Huaijun Zhu School of Modern Post Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing University of Posts and Telecommunications

In order to accurately control the trajectory of dynamic directional rotary steerable drilling,the accurate measurement for toolface of the steerable drilling tool is the ***,the near-bit has strong environmental interference such as vibration,impact,pressure and high temperature,which leads to a large error in the calculated *** this paper,a nonlinear model based on quaternion for the dynamic toolface measurement is *** to the model and noise characteristics,a fusion filtering algorithm(FFA) based on complementary filtering and unscented kalman filtering(UKF) is *** more accurate measurement value of toolface obtained by a complementary filter is used to improve the filtering effect of the UKF to calculate the more accurate *** filtering,extended kalman filtering(EKF) and UKF are used as comparative algorithms to verify the superiority of the FFA for dynamically calculating *** simulation is carried out and shows that the FFA can greatly reduce the influence of strong vibration,impact and gyroscope drift on the dynamic toolface *** toolface error calculated by the FFA is less than 4°.The effectiveness of the FFA for dynamic measurement of the near-bit toolface is verified.

关键词： toolface unscented kalman filtering fusion filtering algorithm accelerometer gyroscope

A New UKF Based Fault Detection Method in Non-linear Systems

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International Journal of Plant Engineering and Management 2006年第3期11卷 179-183页

作者： GE Zhe-xue YANG Yong-min HU Zheng College of Mechatronics Engineering and Automation National University of Defense Technology Changsha 410073 P. R. China

To detect the bias fault in stochastic non-linear dynamic systems, a new unscented kalman filtering（UKF） based real-time recursion detection method is brought forward with the consideration of the flaws of traditional Extended kalman filtering（ EKF）. It uses the UKF as the residual generation method and the Weighted-Sum Squared Residual （WSSR） as the fault detection strategy. The simulation results are provided which demonstrate better effectiveness and a higher detection ratio of the developed methods.

关键词： non-linear stochastic system fault detection unscented kalman filtering sensor

Comparison of UKF and EKF Filter Algorithm in INS/BDS Tightly Mode

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Comparison of UKF and EKF Filter Algorithm in INS/BDS Tightl...

第30届中国控制与决策会议

作者： Zhipeng Chen Ming Bai Jie Lei Yuping Huang Jingsen Wang Xinhai Xia Guangzhou Maritime University

EKF（Extended kalman Filter） is a commonly used filtering method in nonlinear systems. UKF（unscented kalman Filter） is a new nonlinear filtering algorithm, and it’s main characteristic is to transfer the first two moments of the state as EKF, but not to calculate the Jacobian matrix. The aim of this paper is to find that EKF and UKF filtering algorithms which is more suitable for INS（Inertial navigation system）/BDS（Beidou Positioning System） integrated navigation systems. In this paper, the basic principles and characteristics of EKF and UKF have been analyzed, and compared by an experimental. Simulation results show that UKF has the advantages of faster convergence, higher filtering precision and easier realization in INS/BDS tightly integrated navigation

关键词： INS/BDS tight combination unscented kalman filtering Extended kalman Filter

unscented kalman Filter for Nonlinear Systems with One-step Randomly Delayed Measurements and Colored Measurement Noises

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Unscented Kalman Filter for Nonlinear Systems with One-step ...

第37届中国控制会议

作者： Xinmei Wang Zhenzhu Liu Leimin Wang Feng Liu Wei Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In this paper, a new unscented kalman filter（unscented kalman filter, UKF） for nonlinear system with both one-step randomly delayed measurements and colored measurement noises is proposed. Firstly, the first-order Markov sequence model is used to whiten colored noise, at the same time, an independent and identically distributed Bernoulli variable is used to model the delay of measurement data transmission, then the model of nonlinear one-step randomly time delay system with colored noise whitening is established. Secondly, filter recursion formula of UKF under the above model is proposed through unscented transformation（unscented transformation, UT） to calculate the posterior mean and covariance of the nonlinear state based on the Bayesian filter framework. The proposed new UKF method can effectively deal with the issue that traditional UKF is failure under the condition of one-step randomly delayed measurements and colored measurement noises. The efficiency and superiority of the proposed method are illustrated in a numerical example for a target tracking problem.

关键词： State Estimation unscented kalman filtering One-step Randomly Delayed Measurements Colored Measurement Noises