COVARIANCE-BASED NETWORK TASKING OF OPTICAL SENSORS
COVARIANCE-BASED NETWORK TASKING OF OPTICAL SENSORS
复制标题
基于协方差的光学传感器网络任务分配
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
D. Naho’olewa
中科院分区：
文献类型：
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作者：
K. Hill;P. Sydney;K. Hamada;Randy F Cortez;K. Luu;M. Jah;P. Schumacher;Michael Coulman;J. Houchard;D. Naho’olewa
维护常驻空间物体（RSO）目录对于保护空间资产至关重要。然而，目前深空 RSO 的空间监视网络任务是基于临时 RSO 重要性类别系统，并且仅粗略地解释了目录轨道估计中的误差。 TASMAN（多应用网络中的任务自主传感器）是一个全面的网络光学传感器高保真仿真环境，旨在为动态和响应式任务规划算法提供灵活的测试平台。使用 TASMAN 执行的模拟显示了利用 RSO 状态误差协方差（已集中计算的量）来更有效地调度传感器以减少目录状态中的误差的结果。使用基于协方差的调度可以明显提高中值目录准确性。
Maintaining the catalog of Resident Space Objects (RSOs) is of critical importance to the protection of space assets. However, currently the Space Surveillance Network tasking for deep space RSOs is based upon an ad hoc RSO importance category system and only crudely accounts for the error in the catalog orbit estimates. TASMAN (Tasking Autonomous Sensors in a Multiple Application Network) is a comprehensive high-fidelity simulation environment of networked optical sensors that is designed to provide a flexible test-bed for dynamic and responsive mission planning algorithms. Simulations performed using TASMAN show the results of exploiting the RSO state error covariance, a quantity already computed centrally, to more effectively schedule the sensors to reduce error in the catalog states. Significant improvements in the median catalog accuracy are apparent from using covariance-based scheduling.