Forward-backward time varying forgetting factor Kalman filter based DOA estimation algorithm for UAV (Unmanned Aerial Vehicle) autolanding

2002 ◽  
Author(s):  
Jun-seok Lim ◽  
Junil Song ◽  
Koeng-Mo Sung
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Time Varying ◽  
Forgetting Factor ◽  
Aerial Vehicle

scholarly journals Attitude heading reference algorithm based on transformed cubature Kalman filter

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1446-1453
Author(s):  
Yong-jun Yu ◽  
Xiang Zhang ◽  
M Sadiq Ali Khan
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Reference System ◽  
Orthogonal Transformation ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Measurement Unit ◽  
Sampling Point ◽  
Cubature Kalman Filter ◽  
Aerial Vehicle

Stable and accurate attitude estimation is the key to the autonomous control of unmanned aerial vehicle. The Attitude Heading Reference System using micro-electro-mechanical system inertial measurement unit and magnetic sensor as measurement sensors is an indispensable system for attitude estimation of the unmanned aerial vehicle. Aiming at the problem of low precision of the Attitude Heading Reference System caused by the nonlinear attitude model of the micro unmanned aerial vehicle, an attitude heading reference algorithm based on cubature Kalman filter is proposed. Aiming at the nonlocal sampling problem of cubature Kalman filter, the transformed cubature Kalman filter using orthogonal transformation of the sampling point is presented. Meanwhile, an adaptive estimation algorithm of motion acceleration using Kalman filter is proposed, which realizes the online estimation of motion acceleration. The car-based tests show that the algorithm proposed in this paper can accurately estimate the carrier’s motion attitude and motion acceleration without global positioning system. The accuracy of acceleration reaches 0.2 m/s2, and the accuracy of attitude reaches 1°.

Dynamic gyroscope sensors fusion and calibration using Kalman filter

2018 ◽  
Vol 7 (2.3) ◽  
pp. 18
Author(s):  
Mishell D. Lawas ◽  
Sherwin A. Guirnaldo
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Systems Design ◽  
Mean Square ◽  
Fusion Technique ◽  
System Experiment ◽  
Aerial Vehicle ◽  
Sensors Fusion ◽  
The Stability ◽  
Actual Test

The stability of an Unmanned Aerial Vehicle (UAV) during actual flight conditions is one parameter that is very important in systems design in Avionics. In this research, two sensors, the autopilot microcontroller and the smartphone gyroscope sensing mechanism, are fused together and calibrated to monitor the flying behavior of the UAV prior to actual test flights. The two fused sensors and installed inside the UAV for relatively increased sensing accuracy and best flight monitoring capabilities. A Kalman filter is used as fusion technique and a Stewart Motion tracker is also used to test the ruggedness and accuracy of the fused sensor system. Experiment results show that fused system can give an overall mean square error or 1.9729.

The estimation algorithm of operative capabilities of complex countermeasures to resist UAVs

SIMULATION ◽  
2018 ◽  
Vol 95 (6) ◽  
pp. 569-573
Author(s):  
Igor Korobiichuk ◽  
Yuriy Danik ◽  
Oleksyj Samchyshyn ◽  
Sergiy Dupelich ◽  
Maciej Kachniarz
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Estimation Algorithm ◽  
Probability Of Detection ◽  
Software Implementation ◽  
Observation Model ◽  
Different Types ◽  
Aerial Vehicle ◽  
Use Efficiency ◽  
Time Required

The proposed observation model provides for calculating the probability of detection of different types of unmanned aerial vehicle (UAV) at a certain range with regard to their tactical and technical characteristics and security equipment capabilities. The comparison of the obtained values of generalized indicators of security equipment use efficiency is based on a specified criterion. To take into account factors that significantly affect a modeling object, calculations are carried out under specified conditions and restrictions. UAVs should be detected until a covering object gets in a swath width given the time required for countermeasures. Based on the software implementation of the algorithm we have evaluated the efficiency of use of hypothetical security equipment for detecting certain types of UAVs, and defined means of further use or improvement.

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.

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