Real-Time Flight Test Track Filtering and Association Using Kalman Filter and QDA Classifier

Abstract

In this paper an on-line track filtering and association algorithm for flight test was proposed. Firstly, a K-means clustering based scheme was used for track initialization and initial state and corresponding covariance matrix estimation for second-order extended Kalman filter. After that, track filtering and association and frequency estimation of Dutch roll were achieved through interactive use of the second-order extended Kalman-filter and the Kalman-predictor based QDA minimum error rate Bayesian classifier. Experimental results had shown that the algorithm can initialize the track reliably, filter and associate tracks in real time and estimate the frequency of flight testing precisely.