【作者】 庞博；

【导师】 孙兆伟；

【作者基本信息】 哈尔滨工业大学 ， 飞行器设计， 2013， 硕士

【摘要】 近几年，MEMS技术的进步，推动着小型卫星向微型化、轻量化和商品化发展，使纳卫星和皮卫星的出现成为可能，拓宽了小型卫星的空间应用领域。然而，作为小型卫星上的惯性测量元件，商用现货技术下的MEMS陀螺测量精度较低，这极大地限制了其在小型卫星或是其他产品上的应用。针对这种情况，本文将大量相同精度的MEMS陀螺组成批量MEMS陀螺系统，结合信息融合技术，设计了能够提高精度的批量MEMS陀螺信息融合系统。本文的主要研究工作如下：首先，详细分析了MEMS陀螺的误差组成，将随机误差列为本文主要的研究对象，对批量MEMS陀螺数据采集系统硬件进行了说明。介绍了ARMA模型的结构和建模方法，对采集的真实MEMS陀螺随机漂移数据进行预处理，建立了ARMA模型。采用Allan方差法对MEMS陀螺的随机误差进行辨识，并引入DAVAR法来分析MEMS陀螺的动态误差特性。通过Allan方差法建立MEMS陀螺的误差模型，确定了衡量批量MEMS陀螺融合数据精度提高的性能指标。其次，针对MEMS陀螺数据具有弱非线性的特点，引入经验模态分解法对其进行研究。由于普通经验模态分解法的去噪方式有可能造成有用信号的缺失，故在经验模态分解法的基础上引入基于小波熵的小波阈值去噪方法。并根据本征模函数的特性，提出了对批量MEMS陀螺数据进行分阶去噪融合的算法，提高了融合数据的精度。为避免故障MEMS陀螺的信号污染批量MEMS陀螺的融合结果，提出了一种基于支持度的具有容错性质的融合算法。并通过人为地加入硬、软故障信号，验证了这种方法的有效性。最后，考虑到批量MEMS陀螺数量较多这个问题，在之前研究内容的基础上，引入基于Kalman滤波的序贯式估计融合算法，得出一种适用于批量MEMS陀螺系统的多级序贯式容错融合算法，在提高融合结果精度的同时，能够有效排除故障MEMS陀螺对融合结果的影响。更多还原

【Abstract】 In recent years, small satellites have been promoted to be miniaturization,lightweight and commercial by MEMS technology, which makes the appearance ofnano-satellite and pico-satellite possible and broadens the field of spaceapplications for small satellites. However, as a inertial measurement device of asmall satellite, the measurement accuracy of the MEMS gyroscope under COTStechnology is low, which greatly limits the application on small satellites or otherproducts. Aiming at this situation, the thesis will compose a lot of MEMSgyroscopes of the same precision into the multiple MEMS gyroscopes system.Combining the technology of information fusion, the design of information fusionsystem based on multiple MEMS gyroscopes has been made. The contents of thisthesis include:Firstly, the MEMS gyroscope error, which has been analyzed in detail, showsthat the random error would be paid more attention. A data acquisition systemhardware for the multiple MEMS gyroscopes data collection is described. Thestructural and modeling method of ARMA model has been introduced and theARMA model is established by pretreating the random error data of the true MEMSgyroscope.The thesis introduces the Allan variance method to identify the MEMSgyroscope random error, and the dynamics of MEMS gyroscope error characteristicsis analyzed using the DAVAR method. The error model of the MEMS gyroscope isestablished by the Allan variance method，and the performance index to scale theefficiency of the information fusion algorithm is given.Then, Empirical Mode Decomposition is used to study the data of the MEMSgyroscopes which has weak nonlinearity. As ordinary EMD de-noising method maycause loss of useful information, the thesis combines EMD method with waveletthreshold de-noising method based on wavelet entropy. And according to thecharacteristics of intrinsic mode functions, an algorithm that hierarchical de-noseand fusion IMFs is presented, which improves the accuracy of the fusion data.Toavoid the effect of malfunction signal, the thesis proposes a fault-tolerant fusionalgorithm that based on the support degree. The efficiency of the algorithm istestified by artificially adding fault signal caused by the hardware or softwarefaulty.Finally, a fusion algorithm with sequence disposal based on Kalman filter isimplemented with the previous research to construct a kind of multi-levelfault-tolerant sequential fusion algorithm which is suitable for the large number ofMEMS gyroscopes. The result shows that the multi-level fault-tolerant sequential fusion algorithm could improve the accuracy of the fusion data, and effectivelytroubleshoot the fault MEMS gyroscope.更多还原