Decentralized Estimation of Spacecraft Relative Motion Using Consensus Extended Kalman Filter

2018 ◽  
Author(s):  
Jingwei Wang ◽  
Eric A. Butcher
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Decentralized Estimation

scholarly journals Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Sondre Sanden Tørdal ◽  
Geir Hovland
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Degrees Of Freedom ◽  
Load Transfer ◽  
Real Life ◽  
Fusion Algorithm ◽  
Six Degrees Of Freedom ◽  
Motion Estimate

In this paper, a solution for estimating the relative position and orientation between two ships in six degrees-of-freedom (6DOF) using sensor fusion and an extended Kalman filter (EKF) approach is presented. Two different sensor types, based on time-of-flight and inertial measurement principles, were combined to create a reliable and redundant estimate of the relative motion between the ships. An accurate and reliable relative motion estimate is expected to be a key enabler for future ship-to-ship operations, such as autonomous load transfer and handling. The proposed sensor fusion algorithm was tested with real sensors (two motion reference units (MRS) and a laser tracker) and an experimental setup consisting of two Stewart platforms in the Norwegian Motion Laboratory, which represents an approximate scale of 1:10 when compared to real-life ship-to-ship operations.

Relative State Estimation for Spacecrafts in Proximity in the Presence of Sensor Noise

2013 ◽  
Vol 765-767 ◽  
pp. 2299-2304
Author(s):  
Xu Huang ◽  
Ye Yan ◽  
Yang Zhou
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Velocity Estimation ◽  
Light Detection And Ranging ◽  
Dynamical Model ◽  
Lidar System ◽  
Relative State ◽  
Angle Measurements

This paper develops a relative position and velocity estimation approach for spacecrafts in proximity. A dynamical model is built at first to describe the relative motion between the chaser and target. In this approach a light detection and ranging (LIDAR) system is used to perform the range and angle measurements of the target relative to the chaser. The three-axis magnetometer (TAM) and gyro are installed on the chaser to measure the chasers attitude. An extended Kalman filter (EKF) is designed to estimate the relative state by combination of the measurements and dynamical model. Numerical simulations prove the validity of proposed filter.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

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