斜装冗余传感器的分布式导航系统研究

为了满足低成本、高性能惯性导航要求，解决传统单一主惯导系统的成本高、体积大等问题，利用低成本MEMS惯性传感器，采用传感器斜装冗余配置，在最优卡尔曼滤波的基础上提出了一种基于虚拟传感器的最优信息融合技术，简化了测量系统的动态模型，减小计算的同时提高了测量精度。将斜装冗余惯性测量节点安装在载体的不同位置构成基于斜装冗余传感器的分布式导航系统，分析了分布式结构，利用基于虚拟传感器的等效模型，设计了分布式导航系统的测量融合系统。通过仿真试验，校验了基于虚拟传感器的最优信息融合有效地提高了测量精度，基于斜装冗余传感器的分布式导航具有较高的导航精度，同时证明此方法具有一定的抗干扰能力，能够抑制载体局部随机扰动对导航性能的影响。

Research on Distributed Navigation System Based on Skew Redundant Sensors

In order to meet the requirement of inertial navigation system (INS) for low cost and high performance and solve problems of high cost and large volume of the traditional single master INS, low cost MEMS inertial sensors are installed in skew redundant configuration. Based on the Kalman filter, a new optimal data fusion approach based on virtual sensors is proposed, thus the dynamic model of measurement system is simplified, the computational effort is reduced, and the measurement accuracy is improved. The distributed navigation system based on skew redundant sensors is designed by installing multiple skew redundant inertial measurement unit (SRIMU) nodes in different parts of the carrier. With analysis on the sensor network architecture, the measurement fusion of the distribution navigation system is designed by using the equivalent model based on virtual sensors. The simulation results verify that the optimal data fusion based on virtual sensors improves the measurement accuracy and that the distributed navigation system provides the high accuracy navigation information and suppresses the impact of carrier’s local stochastic perturbation on navigation performance.