The classic algorithm in the field of nonlinear estimation is the extended Kalman filter (EKF), which uses Taylor\'s linear transformation to approximate the nonlinear model, and thus has the disadvantages of large amount of calculation, poor real-time performance, and low estimation accuracy. The particle filter uses some random samples (particles) with weights to represent the required posterior probability density, rather than the traditional linear transformation, to obtain an approximate optimal numerical solution based on the physical model, which has the characteristics of high accuracy and fast convergence speed. This paper simulates the classic azimuth and slant range measurement tracking problem. The simulation results show that the tracking performance of the particle filter is better than that of the EKF.
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