自适应两步滤波算法在机载IRSTS被动定位中的应用

Application of Adaptive Two-step Filtering Algorithm to Passive Location of IRSTS

在机载红外搜索跟踪系统被动定位研究中,针对扩展卡尔曼滤波算法要求先验的噪声统计及存在系统观测模型线性化误差影响滤波精度的特点,利用两步滤波算法并结合Sage-Husa噪声估计器构建了适用于机载IRSTS被动定位特点的自适应两步滤波算法模型,算法不仅实时在线地估计了观测噪声的统计特性,而且避免了观测模型线性化误差。仿真结果表明,在完全相同的初始条件下,自适应两步滤波算法对目标运动参数的估计结果明显优于扩展卡尔曼滤波,从而提高了机载IRSTS被动定位的精度。

Aiming at the speciality of transcendental noise statistics and linearization error ot measurement model effecting on filter precision during extended kalman filter with applications to passive location of IRSTS, we constructed adaptive two-step filter algorithm model for passive location of IRSTS by means of integrating two-step filtering algorithm with Sage-Husa noise statistics estimator. It not only approximated realtime measurement subjunctive noise statistics but also avoided linearization error of measurement model. The calculated results of our simulation experiment show the advantage of adaptive two-step filtering algorithm under the same condition. The algorithm enhanced the filtering precision of passive location by IRSTS.