Global Positioning System （GPS） /Inertial Navigation System （INS） integrated system is continuously gaining research interests in many positioning and navigation fields. Kalman filtering-based integrated algorithm has some drawbacks on stability, computation load, robustness, and system observability performances. Based on neural network technology, a new GPS/INS integration filtering algorithm is studied for an integration scheme of the attitude determination GPS/INS integrated navigation system. Through some theoretic analysis, this algorithm not only has good estimation performance, but also has better robustness to the system model and noise than the traditional Kalman algorithm. To assess the performance of the proposed integrated model more deeply, some simulation is done to compare with the traditional Kalman filter model. The results indicate that the proposed model provides a significant improvement in some performance, such as accuracy, stability, robustness, and so on.
在剖析联邦滤波核心理论的基础上,对当前学术热点——联邦滤波器信息分配因子优选问题进行探讨。分析了文献中几种常见的优选算法,指出其中的理论缺陷。从全局估计、局部估计、容错性等三方面质疑了信息分配因子优选的必要性和可行性,提出一些值得商榷之处,并得出结论。通过S IN S/GPS/TAN/SAR四组合导航系统联邦滤波器仿真分析,验证了观点的有效性。
