Abstract: The Extended Kalman Filter （EKF） is the most popular approach to recursive nonlinear estimation.Because it is a linearization technique based on a first order Taylor series expansion of the nonlinear system and measurement functions about the current estimate of the state，it often provides an insufficiently accurate representation in many cases.The particle filtering method has become an important alternative to the EKF.It does not involve linearizations around current estimates but rather represent the desired distributions by discrete random measures，which are composed of weighted particles.It has a high accuracy and a rapid convergence.A simulation example of the bearings-only tracking problem is presented，and the result proves that the performance of the particle filter is greatly superior to that of the EKF.