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首页> 外文期刊>Journal of guidance, control, and dynamics >Gaussian Sum Filters for Space Surveillance: Theory and Simulations
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Gaussian Sum Filters for Space Surveillance: Theory and Simulations

机译：高斯和滤波器用于空间监视：理论与模拟

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摘要

While standard Kalman-based filters, Gaussian assumptions, and eovariance-weighted metrics are very effective in data-rich tracking environments, their use in the data-sparse environment of space surveillance is more limited. To properly characterize non-Gaussian density functions arising in the problem of long-term propagation of state uncertainties, a Gaussian sum filter adapted to the two-body problem in space surveillance is proposed and demonstrated to achieve uncertainty consistency. The proposed filter is made efficient by using only a one-dimensional Gaussian sum in equinoctial orbital elements, thereby avoiding the expensive representation of a full six-dimensional mixture and hence the "curse of dimensionality." Additionally, an alternate set of equinoctial elements is proposed and is shown to provide enhanced uncertainty consistently over the traditional element set. Simulation studies illustrate the improvements in the Gaussian sum approach over the traditional unscented Kalman filter and the impact of correct uncertainty representation in the problems of data association (correlation) and anomaly (maneuver) detection.
机译：虽然基于标准卡尔曼的滤波器，高斯假设和权变加权度量在数据丰富的跟踪环境中非常有效，但它们在空间监视的数据稀疏环境中的使用受到了限制。为了正确地描述在状态不确定性的长期传播问题中出现的非高斯密度函数，提出了一种适用于空间监视中的两体问题的高斯和滤波器，并证明了该不确定性的一致性。通过仅在等轨道轨道元素中使用一维高斯和，可以使提出的滤波器高效，从而避免了完整的六维混合物的昂贵表示，从而避免了“维数的诅咒”。另外，提出了另一套等量元素，并显示出与传统元素集相比一致地提供了增强的不确定性。仿真研究表明，与传统的无味卡尔曼滤波器相比，高斯和方法得到了改进，正确的不确定性表示对数据关联（相关）和异常（操纵）检测的影响。

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