一种适用于量测噪声不确定的组合导航系统数据融合方法
By constructing an inertial/astronomical integrated navigation system model, and using empirical mode decomposition and exponential decay weighted methods to decompose and update the measurement noise covariance, the problem of accuracy degradation caused by measurement noise changes in the inertial/astronomical integrated navigation system is solved, and high-precision, low-complexity data fusion is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- QILU UNIVERSITY OF TECHNOLOGY (SHANDONG ACADEMY OF SCIENCES)
- Filing Date
- 2023-08-07
- Publication Date
- 2026-07-17
AI Technical Summary
Existing Kalman filters cannot adapt to changes in measurement noise in inertial/astronomical integrated navigation systems, leading to decreased or divergent data fusion accuracy. Furthermore, existing adaptive filtering methods suffer from high computational complexity and low accuracy.
By combining empirical mode decomposition and filtering operation mean method with exponential decay weighting method, an inertial/astronomical integrated navigation system model is constructed to decompose the measurement noise sequence and update the measurement noise covariance in real time, thereby improving robustness and computational efficiency.
Under conditions of measurement noise uncertainty, this technology improves the accuracy of navigation data fusion in inertial/astronomical integrated navigation systems, reduces computational complexity, and achieves high accuracy in low-cost systems.
Smart Images
Figure CN117268376B_ABST