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中文摘要：
解决高度非线性并且 non-Gaussian 在地磁气的航行的递归的州的评价问题， unscented 粒子过滤器(UPF ) 被介绍给航行系统。模拟显示用 UPF 的那次地磁气的航行能与大起始的水平位置错误完成位置评价。然而，这个航行系统能仅仅提供位置信息。为了提供所有 kinematics，说飞机，一个新奇自治航行算法，命名 unscented 粒子和 Kalman 混血儿的评价航行算法(UPKHNA ) ，为地磁气的航行被建议。UPKHNA 把 UPF 和气压的高度表的产量用作位置测量，并且采用了 Kalman 过滤器估计飞机的 kinematics 状态。模拟证明用 UPKHNA 的那次地磁气的航行能连续地提供飞机的所有 kinematics 状态评价，并且水平放表演用 UPF 仅仅比那好。
英文摘要：
To solve the highly nonlinear and non-Gaussian recursive state estimation problem in geomagnetic navigation, the unscented particle filter （UPF） was introduced to navigation system. The simulation indicates that geomagnetic navigation using UPF could complete the position estimation with large initial horizontal position errors. However, this navigation system could only provide the position information. To provide all the kinematics states estimation of aircraft, a novel autonomous navigation algorithm, named unscented particle and Kalman hybrid navigation algorithm （UPKHNA）, was proposed for geomagnetic navigation, The UPKHNA used the output of UPF and barometric altimeter as position measurement, and employed the Kahnan filter to estimate the kinematics states of aircraft. The simulation shows that geomagnetic navigation using UPKHNA could provide all the kinematics states estimation of aircraft continuously, and the horizontal positioning performance is better than that only using the UPF.
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