A suboptimal fading quadrature Kalman filter for target tracking

In this paper, the Quadrature Kalman filter (QKF) algorithm of nonlinear systems is studied indepth, with its merits and drawbacks being analyzed. A suboptimal fading QKF algorithm (SFQKF) based on Strong Tacking Filter(STF) is also proposed to adjust the covariance matrix of state prediction error, the covariance matrix of prediction error, and the cross covariance matrix between the state prediction error and the measured prediction error in real time through the time-varied suboptimum fading factor, which can adjust the gain matrix of filter in real time. Moreover, the derivative process of suboptimal fading factor is given. The mechanism analysis and emulation experiment of this algorithm show that SFQKF algorithm, which inherits the excellent performance of Strong Tracking Filter (STF), can overcome the defects of QKF algorithm and have stronger ability to track the states with abrupt changes. Compared with QKF algorithm, the stability of FQKF algorithm is improved by 14.9%, and the amount of calculations required is moderate.