在以Ｋａｌｍａｎ滤波为基础的各种被动式跟踪算法中，一直存在几个难题：初值敏感、估计有偏且对先验信息的强依赖性。本文根据目标－观测站的几何关系，导出了基于伪线性量测的最小二乘被动式跟踪算法，该算法可减少先验信息。ＭｏｎｔｅＣａｒｌｏ模拟结果证明了本方法的有效性和正确性，本文同时给出了其他三种不同的被动式跟踪算法的仿真结果，并进行了简要分析 There are always some problems in the passive tracking algorithms based on Kalman filtering: the initial value is sensitive, the estimation is biased and the strong dependence on prior information. In this paper, based on the geometric relationship between the target and the observatory, the least square passive tracking algorithm based on pseudo-linearity measurement is derived, which can reduce the prior information. MonteCarlo simulation results prove the validity and correctness of this method. In the meantime, the simulation results of the other three different passive tracking algorithms are given and briefly analyzed