scholarly journals Orbit Propagation and Determination of Low Earth Orbit Satellites

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ho-Nien Shou
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Nonlinear Problems ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Positioning Accuracy ◽  
Orbit Propagation

This paper represents orbit propagation and determination of low Earth orbit (LEO) satellites. Satellite global positioning system (GPS) configured receiver provides position and velocity measures by navigating filter to get the coordinates of the orbit propagation (OP). The main contradictions in real-time orbit which is determined by the problem are orbit positioning accuracy and the amount of calculating two indicators. This paper is dedicated to solving the problem of tradeoffs. To plan to use a nonlinear filtering method for immediate orbit tasks requires more precise satellite orbit state parameters in a short time. Although the traditional extended Kalman filter (EKF) method is widely used, its linear approximation of the drawbacks in dealing with nonlinear problems was especially evident, without compromising Kalman filter (unscented Kalman Filter, UKF). As a new nonlinear estimation method, it is measured at the estimated measurements on more and more applications. This paper will be the first study on UKF microsatellites in LEO orbit in real time, trying to explore the real-time precision orbit determination techniques. Through the preliminary simulation results, they show that, based on orbit mission requirements and conditions using UKF, they can satisfy the positioning accuracy and compute two indicators.

Real-time orbit determination of Low Earth orbit satellite based on RINEX/DORIS 3.0 phase data and spaceborne GPS data

2020 ◽  
Vol 66 (7) ◽  
pp. 1700-1712
Author(s):  
Chongchong Zhou ◽  
Shiming Zhong ◽  
Bibo Peng ◽  
Jikun Ou ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Orbit Determination ◽  
Low Earth Orbit ◽  
Gps Data ◽  
Earth Orbit ◽  
Orbit Satellite ◽  
Low Earth Orbit Satellite ◽  
Phase Data

A Method for Real-Time Estimating Pose of Large Aircraft Component in Process of Pose Adjustment Based on the Unscented Kalman Filter

2012 ◽  
Vol 442 ◽  
pp. 251-255
Author(s):  
Zheng Ying
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Kinematic Model ◽  
Estimation Method ◽  
Time Estimation ◽  
Measuring Point ◽  
Aircraft Component ◽  
Forward Kinematic ◽  
Large Aircraft

To estimate the pose of large aircraft component in pose adjustment quickly and accurately, a real-time estimation method based on Unscented Kalman filter (UKF) is proposed. Firstly, in the process of the aircraft component adjustment, a rough value of aircraft component’s pose is acquired by using forward kinematic model and the displacement of positioners on real time. Then, position of a measuring point fixed on aircraft component is obtained by a laser tracker. At last, UKF is employed to integrate the previous rough value and the measuring point position for evaluating the accurate pose of aircraft component. Numerical simulation results show that the presented method is achieved easily, calculated fast and high accurate.

scholarly journals A High-Precision, Real-Time, and Robust Indoor Visible Light Positioning Method Based on Mean Shift Algorithm and Unscented Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1094 ◽  
Author(s):  
Zekun Xie ◽  
Weipeng Guan ◽  
Jieheng Zheng ◽  
Xinjie Zhang ◽  
Shihuan Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Light Emitting Diode ◽  
Mean Shift ◽  
Indoor Navigation ◽  
Positioning Accuracy ◽  
Mean Shift Algorithm

Visible light positioning (VLP) is a promising technology for indoor navigation. However, most studies of VLP systems nowadays only focus on positioning accuracy, whereas robustness and real-time ability are often overlooked, which are all indispensable in actual VLP situations. Thus, we propose a novel VLP method based on mean shift (MS) algorithm and unscented Kalman filter (UKF) using image sensors as the positioning terminal and a Light Emitting Diode (LED) as the transmitting terminal. The main part of our VLP method is the MS algorithm, realizing high positioning accuracy with good robustness. Besides, UKF equips the mean shift algorithm with the capacity to track high-speed targets and improves the positioning accuracy when the LED is shielded. Moreover, a LED-ID (the identification of the LED) recognition algorithm proposed in our previous work was utilized to locate the LED in the initial frame, which also initialized MS and UKF. Furthermore, experiments showed that the positioning accuracy of our VLP algorithm was 0.42 cm, and the average processing time per frame was 24.93 ms. Also, even when half of the LED was shielded, the accuracy was maintained at 1.41 cm. All these data demonstrate that our proposed algorithm has excellent accuracy, strong robustness, and good real-time ability.

scholarly journals Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 750
Author(s):  
Wenkang Wan ◽  
Jingan Feng ◽  
Bao Song ◽  
Xinxin Li
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
State Parameter ◽  
State Observation ◽  
Straight Line ◽  
Distributed Drive Electric Vehicle ◽  
Huber Method ◽  
Yaw Moment

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

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