在传统的无源定位中,时差定位因其复杂度低,定位精度高等优点,广泛应用于电子对抗战中。常用的时差定位算法有Taylor算法和Chan算法等,实际应用中考虑到Chan算法的计算速度快,定位精度较精确和Taylor算法收敛速度快等优势,往往结合Taylor-Chan算法进行联合定位。但当测量误差较大时,Chan算法并不能有效地为Taylor算法提供初值,最终导致最后定位结果发散,使得定位结果出错。针对上述问题,以当前统计模型为基础,文中提出基于扩展卡尔曼滤波的时差定位算法。该算法在Taylor算法需要一个良好的初值估计的基础上,引入了卡尔曼滤波算法,通过最优估计的线性滤波为Taylor算法提供一个稳定的初值。

In the traditional passive localization, the time difference localization was widely used in electronic warfare to locate the localization of target because of its low complexity and high locatization accuracy. Commonly used time difference localization algorithms included Taylor iterative algorithm and Chan algorithm. In practice, the Taylor-Chan algorithm was often used for joint localization, which combines the Chan algorithm with fast calculation, accurate localization result and Taylor's fast convergence. However, with the measurement error has been enlarged, the Chan algorithm cannot effectively provide the initial value for the Taylor algorithm, and finally the final localization result was diverged, which makes the locatization result error. Aiming at the above problems, based on the current statistical model, a time difference localization algorithm based on extended Kalman filter was proposed. According to the Taylor algorithm needs for a good initial value estimation, the Kalman filter algorithm was introduced to provide a stable initial value for the Taylor algorithm through linear filtering of the minimum estimated mean square error.

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四川省科技计划基金资助项目(2018GZ0070,2019DRC0042)