scholarly journals A Novel Fifth-Degree Cubature Kalman Filter for Real-Time Orbit Determination by Radar

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhaoming Li ◽  
Wenge Yang ◽  
Dan Ding ◽  
Yurong Liao
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Orbit Determination ◽  
Nonlinear Function ◽  
State Equation ◽  
Bayesian Filtering ◽  
Measurement Equation ◽  
J2 Perturbation ◽  
Cubature Kalman Filter ◽  
Weighted Integral

A novel fifth-degree cubature Kalman filter is proposed to improve the accuracy of real-time orbit determination by ground-based radar. A fully symmetric cubature rule, approaching the Gaussian weighted integral of a nonlinear function in general form, is introduced, and the specific points and weights are calculated by matching the monomials of degree not greater than five with the exact values. On the basis of the above rule, a novel fifth-degree cubature Kalman filter, which can achieve a higher accuracy than UKF and CKF, is derived under the Bayesian filtering framework. Then, to describe the nonlinear system more accurately, the orbital dynamics equation with J2 perturbation is used as the state equation, and the nonlinear relationship between the radar measurement elements and orbital states is built as the measurement equation. The simulation results show that, compared with the traditional third-degree algorithm, the proposed fifth-degree algorithm has a higher accuracy of orbit determination.

Fifth-degree strong tracking cubature Kalman filter for real-time orbit determination by multiple terminals

2017 ◽  
Vol 25 (8) ◽  
pp. 2195-2203
Author(s):  
李兆铭 LI Zhao-ming ◽  
杨文革 YANG Wen-ge ◽  
丁 丹 DING Dan ◽  
廖育荣 LIAO Yu-rong
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Orbit Determination ◽  
Cubature Kalman Filter

scholarly journals Spherical Simplex-Radial Cubature Quadrature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaoming Li ◽  
Wenge Yang
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
Arbitrary Order ◽  
Nonlinear Function ◽  
Quadrature Rule ◽  
Specific Form ◽  
Bayesian Filtering ◽  
The Novel ◽  
Weighted Integral ◽  
Spherical Simplex

A spherical simplex-radial cubature quadrature Kalman filter (SSRCQKF) is proposed in order to further improve the nonlinear filtering accuracy. The Gaussian probability weighted integral of the nonlinear function is decomposed into spherical integral and radial integral, which are approximated by spherical simplex cubature rule and arbitrary order Gauss-Laguerre quadrature rule, respectively, and the novel spherical simplex-radial cubature quadrature rule is obtained. Combined with the Bayesian filtering framework, the general form and the specific form of SSRCQKF are put forward, and the numerical simulation results indicate that the proposed algorithm can achieve a higher filtering accuracy than CKF and SSRCKF.

An investigation on cubature Kalman filter performance for orbit determination application

2019 ◽  
Vol 41 (10) ◽  
Author(s):  
Paula Cristiane Pinto Mesquita Pardal ◽  
Roberta Veloso Garcia ◽  
Helio Koiti Kuga ◽  
William Reis Silva
Keyword(s):  
Kalman Filter ◽  
Orbit Determination ◽  
Filter Performance ◽  
Cubature Kalman Filter

scholarly journals A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2251 ◽  
Author(s):  
Jikai Liu ◽  
Pengfei Wang ◽  
Fusheng Zha ◽  
Wei Guo ◽  
Zhenyu Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Quadruped Robot ◽  
The State ◽  
Estimation Accuracy ◽  
Robot System ◽  
Calculation Time ◽  
Motion State ◽  
Cubature Kalman Filter

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.

scholarly journals Real-Time On-Orbit Estimation Method for Microthruster Thrust Based on High-Precision Orbit Determination

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qinglin Yang ◽  
Weijing Zhou ◽  
Hao Chang
Keyword(s):  
Real Time ◽  
High Precision ◽  
Orbit Determination ◽  
Estimation Method ◽  
Reference Value ◽  
Space Applications ◽  
Measuring Device ◽  
Cubature Kalman Filter ◽  
Precision Orbit Determination ◽  
Orbit Estimation

In order to enable the micro-nanosatellites equipped with microthrusters to better complete various space applications, it is necessary to estimate the thrust performance of the microthrusters in real-time on orbit. This paper proposes a real-time on-orbit estimation method for microthrust based on high-precision orbit determination. By establishing a high-precision orbit dynamic model, the microthrust generated by a microthruster is modeled as a first-order Markov model, combined with a high-precision GNSS measuring device, and the satellite position is obtained through the cubature Kalman filter algorithm, velocity, and thrust real-time on-orbit estimates. For a thrust of 100 μN, the error accuracy of the on-orbit estimation is 3.98%; for a thrust of 500 μN, the error accuracy is 1.79%; for a thrust of 5 mN, the error accuracy can be reduced to 1.43%; and when the thrust is 500 μN, the accuracy of orbit determination is 16 cm. This method solves the problem that the traditional on-orbit thrust estimation method cannot perform real-time on-orbit estimation of microthrust on the order of hundreds of μN. The real-time on-orbit estimation of microthrust of micro-nanosatellites equipped with microthrusters of the order of hundreds of micronewtons or even several mN to tens of mN has certain reference value.

Research on Non-Gyro Projectile’s Attitude Measurement Based on Improved Unscented Kalman Filter

2011 ◽  
Vol 480-481 ◽  
pp. 1111-1116
Author(s):  
Zhao Hua Liu ◽  
Jia Bin Chen ◽  
Yong Wang ◽  
Chun Lei Song ◽  
Jun Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
State Equation ◽  
Measurement Unit ◽  
Attitude Measurement ◽  
Good Precision ◽  
Measurement Equation ◽  
Novel Method ◽  
Linear State

Considering the control requirements of guided projectile, a novel method is studied that using three low-cost MEMS accelerometers as inertial measurement unit to construct state equation and measurement equation of system, using improved unscented Kalman filter (IUKF) to estimate the state of system. For the system characteristic of linear state equation and nonlinear measurement equation, the improved UKF nonlinear filter algorithm which combines KF and UKF was proposed. At the same time, utilizing minimal skew simplex sampling to reduce the number of sigma points, computational efficiency is enhanced. The simulation experimental results show that using IUKF algorithm can obtains good precision of estimation.

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