Unmanned Aerial Vehicle Flight Data Anomaly Detection and Recovery Prediction Based on Spatio-Temporal Correlation

2021 ◽  
pp. 1-12
Author(s):  
Jie Zhong ◽  
Yujie Zhang ◽  
Jianyu Wang ◽  
Chong Luo ◽  
Qiang Miao
Keyword(s):  
Anomaly Detection ◽  
Unmanned Aerial Vehicle ◽  
Temporal Correlation ◽  
Flight Data ◽  
Aerial Vehicle ◽  
Spatio Temporal

scholarly journals Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Matthew B. Rhudy ◽  
Yu Gu ◽  
Haiyang Chao ◽  
Jason N. Gross
Keyword(s):  
Optical Flow ◽  
Unmanned Aerial Vehicle ◽  
Inertial Sensors ◽  
Wide Field ◽  
Flight Data ◽  
Information Filter ◽  
Full State ◽  
Flow Information ◽  
Aerial Vehicle ◽  
Image Distance

This paper offers a set of novel navigation techniques that rely on the use of inertial sensors and wide-field optical flow information. The aircraft ground velocity and attitude states are estimated with an Unscented Information Filter (UIF) and are evaluated with respect to two sets of experimental flight data collected from an Unmanned Aerial Vehicle (UAV). Two different formulations are proposed, a full state formulation including velocity and attitude and a simplified formulation which assumes that the lateral and vertical velocity of the aircraft are negligible. An additional state is also considered within each formulation to recover the image distance which can be measured using a laser rangefinder. The results demonstrate that the full state formulation is able to estimate the aircraft ground velocity to within 1.3 m/s of a GPS receiver solution used as reference “truth” and regulate attitude angles within 1.4 degrees standard deviation of error for both sets of flight data.

scholarly journals The IMPLEMENTATION OF AUTONOMOUS WAYPOINT IN RECONNAISSANCE PLANE (UNMANNED AERIAL VEHICLE) UAV GALAK-24 USE WITH MISSION PLANNER

2021 ◽  
Vol 2 (Oktober) ◽  
pp. 47-55
Author(s):  
Luthfan Herlambang ◽  
Eko Kuncoro ◽  
Muhamat Maariful Huda
Keyword(s):  
Remote Control ◽  
Unmanned Aerial Vehicle ◽  
Flight Data ◽  
Air Vehicle ◽  
Aerial Vehicle ◽  
The Military

Abstract: UAV or unmanned aerial vehicle is an air vehicle or what we often call an airplane that is controlled without a crew but controlled by a pilot remotely using a remote control. This study uses quantitative experiment methods because in this study it must be carried out when the UAV is flying using the autonomous waypoint method. Running the UAV with the autonomous waypoint method, we can use the Mission Planner software. First, we have to install the application on the mission planner and install pixhawk on the UAV which will act as the UAV brain that will receive and execute flight commands that will be sent by the mission planner later. The mission planner can also directly display flight data such as UAV altitude, UAV speed, UAV location, then the mission planner can also store flight data run by the UAV. The Autonomous waypoint method has been widely used in the military field, such as to carry out attacks on the enemy, reconnaissance, and patrol in an area quickly, and can also reduce casualties during combat operations.

scholarly journals APPROXIMATE ASSESSMENT OF THE OPERATIONAL PERFORMANCES OF AN UNMANNED AERIAL VEHICHLE ACCORDING TO ITS FLIGHT DATA

Aviation ◽  
2016 ◽  
Vol 19 (4) ◽  
pp. 187-193 ◽  
Author(s):  
Valeriy Silkov ◽  
Mykola Delas
Keyword(s):  
Fuel Consumption ◽  
Unmanned Aerial Vehicle ◽  
Comparative Assessment ◽  
Flight Duration ◽  
Flight Data ◽  
Flight Range ◽  
Different Types ◽  
Aerial Vehicle ◽  
Drag Ratio ◽  
Lift To Drag Ratio

The article is dedicated to the substantiation of the complex parameter that characterizes the technical level of an unmanned aerial vehicle (UAV). This parameter includes the maximum lift-to-drag ratio, propeller efficiency, specific fuel consumption, and other components, on which the main flight characteristics, such as flight range and flight duration, depend. To make a comparative assessment of UAVs of different types, a special scale is developed.

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

2018 ◽  
Vol 122 (1252) ◽  
pp. 889-912
Author(s):  
Subrahmanyam Saderla ◽  
Dhayalan Rajaram ◽  
A. K. Ghosh
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Unmanned Aerial Vehicle ◽  
Rectangular Cross Section ◽  
A Priori ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Estimated Parameters ◽  
Aerial Vehicle ◽  
Cramer Rao Bound

ABSTRACTThe current research paper describes the lateral-directional parameter estimation from flight data of a miniature Unmanned Aerial Vehicle (UAV) using Maximum Likelihood (ML), and Neural-Gauss-Newton (NGN) methods. An unmanned configuration with a cropped delta planform and thin rectangular cross-section has been designed, fabricated and instrumented. Exhaustive full-scale wind-tunnel tests were performed on the UAV to extract the form of aerodynamic model that has to be postulated a priori for parameter estimation. Rigorous flight tests have been performed to acquire the flight data for several prescribed manoeuvres. Four sets of compatible flight data have been used to carry out parameter estimation using classical ML and neural-network-based NGN methods. It is observed that the estimated parameters are consistent and the lower values of the Cramer-Rao bound for the corresponding estimates have shown significant confidence in the obtained parameters. Furthermore, to validate the aerodynamic model used and to enhance the confidence in the estimated parameters, a proof of match exercise has been carried out.

Sign in / Sign up

Export Citation Format

Share Document