State Estimation of a Nonlinear Unmanned Aerial Vehicle Model using an Extended Kalman Filter

2008 ◽  
Author(s):  
Caterina Grillo ◽  
Francesco Vitrano
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unmanned Aerial Vehicle ◽  
Extended Kalman Filter ◽  
Vehicle Model ◽  
Aerial Vehicle

scholarly journals Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

2017 ◽  
Vol 9 (3) ◽  
pp. 169-186 ◽  
Author(s):  
Kexin Guo ◽  
Zhirong Qiu ◽  
Wei Meng ◽  
Lihua Xie ◽  
Rodney Teo
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Extended Kalman Filter ◽  
Ultra Wideband ◽  
Cooperative Localization ◽  
Relative Localization ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Displacement Measurements

This article puts forward an indirect cooperative relative localization method to estimate the position of unmanned aerial vehicles (UAVs) relative to their neighbors based solely on distance and self-displacement measurements in GPS denied environments. Our method consists of two stages. Initially, assuming no knowledge about its own and neighbors’ states and limited by the environment or task constraints, each unmanned aerial vehicle (UAV) solves an active 2D relative localization problem to obtain an estimate of its initial position relative to a static hovering quadcopter (a.k.a. beacon), which is subsequently refined by the extended Kalman filter to account for the noise in distance and displacement measurements. Starting with the refined initial relative localization guess, the second stage generalizes the extended Kalman filter strategy to the case where all unmanned aerial vehicles (UAV) move simultaneously. In this stage, each unmanned aerial vehicle (UAV) carries out cooperative localization through the inter-unmanned aerial vehicle distance given by ultra-wideband and exchanging the self-displacements of neighboring unmanned aerial vehicles (UAV). Extensive simulations and flight experiments are presented to corroborate the effectiveness of our proposed relative localization initialization strategy and algorithm.

Unmanned aerial vehicle for transmission line inspection using an extended Kalman filter with colored electromagnetic interference

2020 ◽  
Vol 100 ◽  
pp. 322-333 ◽  
Author(s):  
Mathaus Ferreira da Silva ◽  
Leonardo M. Honório ◽  
Andre Luis M. Marcato ◽  
Vinicius F. Vidal ◽  
Murillo F. Santos
Keyword(s):  
Kalman Filter ◽  
Transmission Line ◽  
Unmanned Aerial Vehicle ◽  
Extended Kalman Filter ◽  
Electromagnetic Interference ◽  
Aerial Vehicle

Nonlinear estimator design based on extended Kalman filter approach for state estimation of articulated heavy vehicle

2018 ◽  
Vol 233 (2) ◽  
pp. 254-265 ◽  
Author(s):  
Hamze Ahmadi Jeyed ◽  
Ali Ghaffari
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
High Velocity ◽  
The State ◽  
Heavy Vehicle ◽  
Lane Change ◽  
Vehicle Model ◽  
Articulated Vehicle ◽  
Simulation Results

In this article, in order to measure the state variables directly in an articulated heavy vehicle, the extended Kalman filter approaches are proposed. For this purpose, using Kane’s method, a nonlinear model is developed for the articulated vehicle, including the motion equations of longitudinal, lateral, and yaw motion of the tractor, and the hitch articulation angle between the tractor and the semi-trailer. Using TruckSim software, the articulated vehicle model is verified through high-velocity lane change maneuver (a single sinusoidal wave with an amplitude of 5° and a frequency of 0.5 Hz) under the dry and slippery road condition. The simulation results showed that the proposed model is close to the real vehicle model and can be used in the estimator development. Then, the state estimation algorithm is designed and implemented using extended Kalman filter for real-time estimation of the states. To evaluate the performance of the extended Kalman filter, simulations with two maneuvers including high-velocity lane change maneuvers in the dry road and slippery road are carried out. The simulation results demonstrate the impressive performance of the extended Kalman filter for state estimation of the articulated vehicle in critical conditions such as the slippery road and the high velocity.

scholarly journals Features of Invariant Extended Kalman Filter Applied to Unmanned Aerial Vehicle Navigation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2855 ◽  
Author(s):  
Nak Ko ◽  
Wonkeun Youn ◽  
In Choi ◽  
Gyeongsub Song ◽  
Tae Kim
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Extended Kalman Filter ◽  
Angular Rate ◽  
Practical Implementation ◽  
State Variables ◽  
Internal Parameters ◽  
New Variant ◽  
Aerial Vehicle ◽  
And Performance

This research used an invariant extended Kalman filter (IEKF) for the navigation of an unmanned aerial vehicle (UAV), and compared the properties and performance of this IEKF with those of an open-source navigation method based on an extended Kalman filter (EKF). The IEKF is a fairly new variant of the EKF, and its properties have been verified theoretically and through simulations and experiments. This study investigated its performance using a practical implementation and examined its distinctive features compared to the previous EKF-based approach. The test used two different types of UAVs: rotary wing and fixed wing. The method uses sensor measurements of the location and velocity from a GPS receiver; the acceleration, angular rate, and magnetic field from a microelectromechanical system-attitude heading reference system (MEMS-AHRS); and the altitude from a barometric sensor. Through flight tests, the estimated state variables and internal parameters such as the Kalman gain, state error covariance, and measurement innovation for the IEKF method and EKF-based method were compared. The estimated states and internal parameters showed that the IEKF method was more stable and convergent than the EKF-based method, although the estimated locations, velocities, and altitudes of the two methods were comparable.

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