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Realization Of An Autonomous Integrated Suite Of Strapdown Astro-inertial Navigation Systems Using Unscented Particle Filtering

机译:利用无味粒子滤波实现捷联惯性导航系统自主集成套件

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Multisensor navigation data synthesis (MNDS) is the process of fusing outputs from inertial sensors with information from other sensors and information processing blocks into one representational form. This technique is anticipated to accomplish enhanced accuracy and more specific inferences than could be achieved by the use of a single sensor alone. Therefore, this research work expounds innovative filtering methodology for the multisensor navigation data synthesis for a ballistic missile application that augments navigation system performance. The premise and characteristics of strapdown inertial navigation system (SINS) integrated with the astronavigation system (ANS) based on the unscented particle filter (UPF) are investigated in this paper. Configuration of the integrated navigation system is presented with its canonical model, and system dynamic and stochastic models required for the filtering algorithm are presented. To exemplify integrated navigation filter mechanization is the foremost aspiration of this research. To validate and corroborate the designed MNDS technique, simulations are carried out that demonstrate the validity of this method on enhancing the navigation system's accuracy with estimation and compensation for the gyro's drift. This integrated system results in a significant reduction in impact-point dispersion of a re-entry vehicle.
机译:多传感器导航数据合成(MNDS)是将惯性传感器的输出与其他传感器和信息处理模块的信息融合为一种表示形式的过程。与仅使用单个传感器相比,该技术有望实现更高的准确性和更具体的推断。因此,本研究工作阐述了用于弹道导弹应用的多传感器导航数据综合的创新过滤方法,可增强导航系统的性能。研究了基于无味粒子滤波器(UPF)的捷联惯性导航系统(SINS)与航天导航系统(ANS)集成的前提和特点。介绍了组合导航系统的配置及其规范模型,并提出了过滤算法所需的系统动态和随机模型。举例说明集成导航滤波器的机械化是这项研究的首要目标。为了验证和证实所设计的MNDS技术,进行了仿真,证明了该方法在通过估计和补偿陀螺仪漂移来增强导航系统的准确性方面的有效性。该集成系统大大降低了重返车辆的冲击点分散性。

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