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Multisensor fusion for an experimental airship based on strong tracking filter

机译:基于强追踪滤波器的实验飞艇的多传感器融合

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摘要

To improve the independent ability of attitude determination and positioning for an unmanned experimental airship platform, a micro inertial measurement system (MIMS) is expected to integrate with the existing system, which incorporates a digital magnetic compass and a differential pseudorange GPS receiver. The navigation error of the low-precision MIMS will be calibrated using nondrift DGPS receiver and magnetic compass. This paper proposes an adaptive strong tracking filter to perform multisensor fusion to assure state-error estimation of convergence under some uncertain conditions. These uncertainties include model simplification, unknown microsensor stochastic characteristics, a large-scale initial filtering parameter variation, and state sudden change. Monte Carlo simulations demonstrate the filter has strong robustness to all the uncertainties mentioned above. By this filtering approach, the navigation errors of MIMS are limited to a certain range. Accordingly, the whole integrated measurement system will respond to dynamics, and its automotive navigation ability is also enhanced.
机译:为了提高无人驾驶实验飞艇平台的独立姿态确定和定位能力,预计将与现有系统集成,该态度测量系统(MIMS)集成了数字磁指南针和差分伪橙色GPS接收器。使用Nondrift DGPS接收器和磁罗盘将校准低精度MIM的导航误差。本文提出了一种自适应强大的跟踪滤波器,以执行多传感器融合,以确保在某些不确定条件下的趋同状态误差。这些不确定性包括模型简化,未知的微传感器随机特性,大规模的初始滤波参数变化和状态突然变化。 Monte Carlo仿真展示过滤器对上述所有不确定性具有强大的鲁棒性。通过这种过滤方法,MIMS的导航误差仅限于一定范围。因此,整个集成测量系统将响应动态,其汽车导航能力也得到增强。

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