仿真结果表明，对于纯方位跟踪问题，U PF不仅解决了扩展卡尔曼滤波器的线性化损失难题，而且与PF等粒子滤波器相比，具有更高的跟踪精度。

The results show that the UPF not only solves the linearized loss problem in the extended Kalman filter, but also is more accurate than the PF in the BOT.

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目前，实现定位跟踪的算法有很多，如卡尔曼滤波算法、扩展卡尔曼滤波算法、粒子滤波算法等。

At present, there are many algorithms to achieve position tracking, such as the Kalman filter algorithm, extended Kalman filter algorithm, particle filter algorithm and so on.

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最后，给出了扩展卡尔曼滤波算法、无迹卡尔曼滤波算法和粒子滤波算法的推导过程和仿真分析。

Finally, the computational procedures and simulation analysis of extended Kalman filtering algorithm, unscented Kalman filtering algorithm and particle filtering algorithm is presented.

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