Two-Stage Kalman Filter for Estimation of Wind Speed and UAV States by using GPS, IMU and Air Data System

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this study, an estimation algorithm based on a two-stage Kalman filter (TSKF) was developed for wind speed and Unmanned Aerial Vehicle (UAV) motion parameters. In the first stage, the wind speed estimation algorithm is developed by the usage of GPS measurements and dynamic pressure measurements. For this purpose, Extended Kalman Filter (EKF) was designed. The wind speed is estimated by the EKF using GPS and pitot tube measurements. Here, the wind speed components and pitot scale factor are considered as state vector variables. As there is no information of the state dynamics, dynamic equations are expressed in this case by random walk process. In the second stage, the estimation of the state parameters of the UAV dynamic model was made based on the Air Data System (ADS) and IMU measurements by using the Linear Kalman filter (LKF). The second stage filter uses ADS pitot tube, angle of attack and side sleep angle measurements, IMU attitude angle and velocity measurements, and the first stage EKF estimates of the wind speed values.

Original languageEnglish
Pages (from-to)60-65
Number of pages6
JournalWSEAS Transactions on Electronics
Volume10
Publication statusPublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 World Scientific and Engineering Academy and Society. All Rights Reserved.

Keywords

  • Air Data System
  • GPS
  • Kalman filter
  • Pitot tube
  • State estimation
  • Unmanned Aerial Vehicle
  • Wind speed

Fingerprint

Dive into the research topics of 'Two-Stage Kalman Filter for Estimation of Wind Speed and UAV States by using GPS, IMU and Air Data System'. Together they form a unique fingerprint.

Cite this