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首页> 外文期刊>Journal of guidance, control, and dynamics >Two-Stage Point Mass Filter on Terrain Referenced Navigation for State Augmentation
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Two-Stage Point Mass Filter on Terrain Referenced Navigation for State Augmentation

机译：用于状态增强的地形参考导航上的两阶段点质量滤波器

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摘要

TSPMF for state augmentation on TRN was proposed. PMF wassuitable for application to TRN because it had the robustness tosignificant position error and nonlinearity of measurement equation.However, it had a disadvantage of computation complexity when thestate variables were augmented. Thus, PMF usually used two or threestate variables (latitude and longitude without or with height).TSPMF is designed to maintain the estimated performance withoutimposing computational burden even if the state variable is augmented.The nonlinear state variables (latitude and longitude) wereestimated using the general PMF, whereas the linear state variable(altitude) was estimated using a single Kalman filter. At this time,specific information (Kalman gain and prior covariance of nonlinearfilter) should be transferred to the linear filter and converted into aform that can be used through moment matching. Simulation resultsshowed that estimation performances of 3D PMF, RB-PMF, andTSPMF were almost similar. However, the computation time ofTSPMF was shorter than RB-PMF. In this paper, only three statevariables were considered because it was challenging to implement three or more dimensional PMF. However, if the proposed algorithmis applied, it can be applied to more than three state variables withoutdegrading its estimation performance or computation burden.
机译：提出了将TSPMF用于TRN的状态增强。 PMF具有对显着的位置误差和测量方程非线性的鲁棒性，因此适合于TRN。但是，当状态变量增加时，它具有计算复杂性的缺点。因此，PMF通常使用两个或三个状态变量（不带高度或带高度的经度和纬度）.TSPMF旨在保持估计的性能而不会增加计算负担，即使状态变量增加了也是如此。非线性状态变量（经度和纬度）使用一般PMF，而线性状态变量（高度）是使用单个卡尔曼滤波器估算的。这时，应将特定信息（卡尔曼增益和非线性滤波器的先验协方差）传递到线性滤波器，并转换为可通过力矩匹配使用的形式。仿真结果表明，3D PMF，RB-PMF和TSPMF的估计性能几乎相似。但是，TSPMF的计算时间比RB-PMF短。在本文中，仅考虑了三个状态变量，因为实现三维或更多维的PMF具有挑战性。但是，如果应用所提出的算法，则可以将其应用于三个以上的状态变量，而不会降低其估计性能或计算负担。

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