|Abstract:||Tethered satellite system (TSS) by virtue of its unique structure is a promising way to study satellite formation, Earth ionosphere, electric generation, and space debris removal. Navigation is a key factor for the TSS to have a stable deployment process, as well as the maintenance stage. However, for the tethered Nano-satellites system (TNSS), there is no enough space for them to equip with micro-wave radar or other high power consumption sensors, so small and low-cost sensors are needed in our research program. In order to control the TSS precisely, the navigation issues including the absolute positioning algorithms and relative positioning scenario are investigated. In the absolute navigation problem, two failure conditions are studied. One is the GPS integrity monitoring, which is to identify the failure satellite and isolate it for the final solution by the progressive sample consensus (PROSAC) method. The other one is the GPS receiver failure. To improve the accuracy of Inertial Measurement Unit (IMU) navigation during this outage, an input-delay neural network (IDNN) is used to training IMU error model when GPS is availability. Unlike the traditional IDNN method only used the delayed input, this study employs the delayed target output as an additional input for training IDNN. In the relative navigation, a strategy is proposed by integrating the relative angles from camera and distance measurement from Xbee. Finally, tests and simulations validate the presented algorithms.|
Proceedings of the ION 2017 Pacific PNT Meeting
May 1 - 4, 2017
Marriott Waikiki Beach Resort & Spa
|Pages:||202 - 210|
|Cite this article:||
Liu, Ming, Zhan, Xingqun, Tu, Jiaxun, Liu, Baoyu, Zhu, Z.H., "Integrated Navigation for Tethered Nano-Satellite System by Modified Input-Delay Neural Networks and PROSAC," Proceedings of the ION 2017 Pacific PNT Meeting, Honolulu, Hawaii, May 2017, pp. 202-210.
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