带渐消因子的Quadrature卡尔曼滤波

为了解决无源传感器机动目标跟踪系统非线性较强、传统的跟踪滤波方法不稳定容易发散的缺陷，提出了一种带渐消因子的QKF（FQKF）算法。该算法通过引入时变渐消因子来实时调整状态预测误差协方差阵、量测预测误差协方差阵及状态预测误差和量测预测误差之间的互协方差阵,利用公式推导得出渐消因子实际上是对状态传播积分点和量测传播积分点进行渐消，进而达到实时调整滤波器增益矩阵的目的。并通过算法的机理分析和仿真实验表明FQKF算法具有强跟踪滤波器（STF）的优良性能，能够克服QKF算法的缺陷，对于无源传感器机动目标跟踪中系统的突变状态具有较强的跟踪能力，较QKF算法稳定性有所提高，并且计算量适中。

A Fading Quadrature Kalman Filter for Nonlinear Systems

Quadrature Kalman Filter（QKF） for the highly non linear bearing only tracking systems is investigated, and its shortcomings are analyzed. To overcome the limitations of QKF, a fading Quadrature Kalman Filter（FQKF）based on Strong Tracking Filter (STF) is presented. The FQKF could adjust its filtering gain matrix on line by introducing a time varying fading factor. Then the algorithm mechanism analysis and simulation results show that FQKF has the advantages of STF, and maintains good performance for sudden changing systems. Thus FQKF’s stability increases and has acceptable complexity compared with QKF.