Earticle

초록

영어

We propose a moving target tracking algorithm using the measurement signals of time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) in this paper. The geolocation system using TDOA measurement does not have enough accuracy to estimate the position of target. We use both TDOA and FDOA measurement signals to estimate the target location and target’s velocity at discrete times. The Kalman filter performs remarkably in calculation and location estimation. However, the estimation error can be large when the priori noise covariances are assumed with improper values. Therefore, we offer an adaptive extended Kalman filter (AEKF) to update the noise covariance at every measurement and estimation process to find proper noise covariance at each steps. The simulation results show our proposed algorithm reduces the position error effectively and improves the accuracy of target tracking greatly.

목차

Abstract
 1. Introduction
 2. System Modeling for Target Localization
 3. Localization Using Adaptive Extended Kalman Filter
 4. Simulation results
 5. Conclusions
 References

키워드

저자

• Dongkyun Kim [ Sungkyunkwan University ]
• Jihyun Ha [ Sungkyunkwan University ]
• Kwanho You [ Sungkyunkwan University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605