MEMS陀螺/半捷联稳定平台信息融合技术

Data Fusion of MEMS Gyroscopes and Semi-Strapdown Signal of Seeker Platform

采用小型MEMS陀螺敏感导引头平台和利用导航IMU的陀螺信息和框架角速度信息共同解算的半捷联技术是目前导引头小型化和结构紧凑化的最有效途径。但是,目前采用这两种方法进行导引头稳定平台控制与制导信息生成时与传统导引头控制方法相比仍存在噪声较大的问题。本文利用“虚拟陀螺”技术将这两种信号相互融合,提升信号精度、降低噪声。在分析半捷联信号生成原理、信号特性的基础上,建立了其噪声模型,利用该模型并结合MEMS陀螺的噪声模型,建立了MEMS陀螺和半捷联信号融合系统的状态方程和量测方程,并利用卡尔曼滤波方法实现对真实信号的估计,采用连续卡尔曼理论分析了稳态条件下模型中关键参数对滤波效果和带宽的影响。仿真和实验结果表明,融合系统能够大幅降低原始信号的噪声。

It is the most effective way to miniaturize and compact the seeker at present by using small MEMS gyroscopes sensitive seeker platform and the semi-strapdown technology resolving together with the gyro information and frame angular velocity information of the navigation IMU.However,compared with the traditional seeker control methods,there is still a problem of large noise when using these two methods to control the seeker stability platform and generate guidance information.In this paper,the"virtual gyro"technology is used to fuse the two signals to improve the signal accuracy and reduce the noise.Based on the analysis of the generation principle and signal characteristics of the semi-strapdown signal,the noise model is established.Using this model and the noise model of MEMS gyroscope,the state equation and measurement equation of MEMS gyroscope and semi-strapdown signal fusion system are established,and the Kalman filter method is used to estimate the real signal.The influence of key parameters in the model on the filtering effect and bandwidth under steady-state conditions is analyzed by using continuous Kalman theory.Simulation and experimental results show that the fusion system can greatly reduce the noise of the original signal.