仿真结果表明，对于纯方位跟踪问题，UPF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.
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.
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.
由于扩展卡尔曼滤波必须假定噪声服从高斯分布，若用于复杂非线性系统，其估计精度不甚理想。粒子滤波对噪声类型没有限制，正在成为非线性系统状态估计的有效近似方法。
Because EKF must assume that the noise is subject to Gaussian distribution, the estimate accuracy is not so good if it is used to estimate the state of complicated nonlinear system.
由于扩展卡尔曼滤波必须假定噪声服从高斯分布，若用于复杂非线性系统，其估计精度不甚理想。粒子滤波对噪声类型没有限制，正在成为非线性系统状态估计的有效近似方法。
Because EKF must assume that the noise is subject to Gaussian distribution, the estimate accuracy is not so good if it is used to estimate the state of complicated nonlinear system.
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