Extended Kalman Filter and Observability Analysis for Consensus Estimation of Spacecraft Relative Motion

2018 ◽  
Author(s):  
Jingwei Wang ◽  
Eric A. Butcher ◽  
Tansel Yucelen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Observability Analysis

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.

scholarly journals EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4174 ◽  
Author(s):  
Rodrigo Munguía ◽  
Sarquis Urzua ◽  
Antoni Grau
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dynamic Models ◽  
Model Parameters ◽  
State Variables ◽  
Test Bed ◽  
Estimation Process ◽  
Observability Analysis ◽  
Aerial Vehicles ◽  
Aerial Vehicle

This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.

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 Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

2018 ◽  
Vol 3 (1) ◽  
pp. 115-127 ◽  
Author(s):  
Emrah Zerdali ◽  
Murat Barut
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Induction Motors ◽  
Viscous Friction ◽  
Speed Sensorless ◽  
Load Torque ◽  
Observability Analysis ◽  
Friction Term ◽  
Flux Estimation

Abstract This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.

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