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首页> 外文会议>Symposium Gyro Technology 2002 Sep 17-18, 2002 Stuttgart, Germany >Studies on Four Terrain Referenced Navigation Techniques
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Studies on Four Terrain Referenced Navigation Techniques

机译：四种地形参考导航技术研究

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This paper deals with different Terrain Referenced Navigation (TRN) techniques. A specially adapted Extended Kalman Filter (EKF) is the core of first TRN technique. In this EKF a digital terrain elevation map is considered as a non-linear measurement function from which a measurement model is derived, if terrain height measurements are processed during the update step of this filter. The second technique is based on the comparison of a set of terrain height measurements with different sets of terrain height data taken from a digital terrain elevation map. This comparison is performed by calculating the correlation of measurements and map data. Another way to determine position estimates is by calculating the complete probability density function (PDF) that is describing the stochastic process of according position. This PDF is changing in time and with new incoming measurements. In the third TRN technique this PDF is approximated by a set of discrete values placed on an equidistant grid. In the fourth technique, a particle filter, is used to approximate the PDF by a high number of samples drawn from according distribution. Compared with a Kalman Filter the last two techniques promises better results because of the possibility to handle non-Gaussian distributions and non-linear system and measurement models.
机译：本文介绍了不同的地形参考导航（TRN）技术。经过特殊修改的扩展卡尔曼滤波器（EKF）是首个TRN技术的核心。在此EKF中，如果在此过滤器的更新步骤中处理了地形高度测量值，则将数字地形高程图视为一个非线性测量函数，可以从中得出测量模型。第二种技术基于一组地形高度测量值与一组从数字地形高程图获取的不同地形高度数据的比较。通过计算测量值与地图数据的相关性来执行此比较。确定位置估计值的另一种方法是通过计算描述相应位置的随机过程的完整概率密度函数（PDF）。该PDF随时间变化并且具有新的传入度量。在第三种TRN技术中，此PDF通过放置在等距网格上的一组离散值来近似。在第四种技术中，使用粒子过滤器通过根据分布分配的大量样本来近似PDF。与卡尔曼滤波器相比，由于可以处理非高斯分布以及非线性系统和测量模型，后两种技术有望带来更好的结果。

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