سال انتشار: ۱۳۹۱

محل انتشار: کنفرانس بین المللی مدل سازی غیر خطی و بهینه سازی

تعداد صفحات: ۷

نویسنده(ها):

Mehdi Darbandi – Department of Electrical Engineering and Computer Science at Iran University of Science and Technology (IUST
Mohammad Abedi –

چکیده:

This paper reviews the techniques used by various filters to ensure covariance consistency under non-linear tracking situations. In addition to stateestimates, filters such as the Kalman filter provide an estimation covariance matrix, which quantifies the accuracy of the state estimate. In applications such asair traffic control, the state estimation covariance is used to predict target future position region. In the polar measurement situation, the original Kalmanfilter is usually replaced by an extended Kalman filter or a converted measurement Kalman filter, but the consistency of the state estimation covariance is no longer guaranteed. This paper compares the estimation covariance consistency of the classic converted measurement Kalman filter, the modified unbiased converted measurement Kalman filter, and the particle filter. From the simulation results of anaircraft tracking scenario, all three filters have goodcovariance consistency under small azimuth noise. For large azimuth noise, the particle filter has the bestconsistency, while the classic converted measurement Kalman filter has very poor consistency