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首先用扩展卡尔曼滤波算法构建了机载红外搜索跟踪系统被动定位滤波模型,然后针对该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,利用虚拟噪声技术,提出了适合于红外搜索跟踪系统被动定位的自适应扩展卡尔曼滤波算法,该算法实时地估计了虚拟噪声的统计特性,减小了线性化误差,提高了非线性滤波的精度。仿真结果表明,在完全相同的初始条件下,自适应扩展卡尔曼滤波对目标距离和速度的估计结果明显优于扩展卡尔曼滤波,此算法具有很高的工程应用价值。
First, the passive Kalman filter algorithm is used to build a passive positioning filter model of airborne infrared search and tracking system. Then, according to the noise characteristics of the filtering algorithm which requires a priori and the linearization error of the system observation model, An adaptive extended Kalman filtering algorithm suitable for passive location of infrared search and tracking system is proposed. This algorithm estimates the statistical properties of virtual noise in real time, reduces the linearization error and improves the accuracy of nonlinear filtering. The simulation results show that under the same initial conditions, the results of the adaptive extended Kalman filter are obviously superior to the extended Kalman filter in estimating the target distance and velocity. This algorithm has high engineering application value.