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Filtering When Object Custody is Ambiguous

机译:对象保管不明确时进行过滤

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Filtering involves predicting the future state of a space object in orbit about the earth given observations (e.g. angles-only or radar measurements) about its current and past states. The task is simplest when the identity of the object is known. A recently developed “Adapted STructural (AST)” coordinate system enables the task to be carried out in a computationally efficient manner. Propagation for a single state (or a small number of sigma points) can be carried out using Keplerian dynamics or using a numerically more expensive propagator to accommodate perturbation effects. In either case, the uncertainty can be represented in AST coordinates as Gaussian to a high level of accuracy. An Unscented Kalman Filter (UKF) has been developed in this situation; in particular, there is no need to use particle filters. However, when object custody is uncertain, i.e. when the latest observation might correspond to two or more objects in a catalog, the filtering task is more complicated. In this case we propose a mixture of Gaussians in AST coordinates to represent the state. The paper will demonstrate the feasibility of this approach.
机译:过滤包括根据给定的空间物体当前和过去状态的观察结果(例如仅角度或雷达测量结果)来预测空间物体在地球轨道上的未来状态。当对象的身份已知时,该任务最简单。最近开发的“自适应结构(AST)”坐标系使任务能够以计算有效的方式执行。单个状态(或少量sigma点)的传播可以使用Keplerian动力学或使用数字上更昂贵的传播器来实现,以适应扰动效应。在这两种情况下,都可以将AST坐标中的不确定性表示为高斯,以达到较高的准确性。在这种情况下,开发了无味卡尔曼滤波器(UKF)。特别地,不需要使用颗粒过滤器。但是,如果不确定对象的保管,即最新的观察结果可能对应于目录中的两个或更多对象,则过滤任务将变得更加复杂。在这种情况下,我们建议在AST坐标中混合使用高斯来表示状态。本文将证明这种方法的可行性。

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