组合导航系统误差分析与补偿理论及方法研究.doc

组合导航系统误差分析与补偿理论及方法研究组合导航系统误差分析与补偿理论及方法研究 - 论文标题:组合导航系统误差分析与补偿理论及方法研究Researches on the Theories and Algorithms of the Error Analysis and Compensation for Integrated Navigation System论文作者 论文导师 杨元喜,论文学位 硕士,论文专业 大地测量学与测量工程论文单位 解放军信息工程大学,点击次数 17,论文页数 93页File Size5607K2007-04-20论文网 /lunwen_431588212/ Inertial Navigation; Components Errors; Wavelet Transform; GPS/INS Integrated Navigation System; GPS/DR Integrated Navigation System; FOG North Seeker惯性导航系统与卫星导航系统相结合是提高导航精度和可靠性的重要途径之一。然而惯导系统的惯性元件误差是影响惯性导航以及组合导航精度的重要因素。本文引入了抗差谱分析、小波分析和自适应滤波等方法,主要对惯性导航系统误差模型建立、误差补偿和控制方法等进行了研究,并将结果应用于陀螺仪寻北、捷联惯导初始对准和组合导航中。论文的主要内容概括如下: 1.针对陀螺信号中的低频有色噪声,对其进行拟合和预报,然后对信号进行抗差谱分析,以期将有用信号、有色噪声以及周期噪声相分离。利用模拟数据对该方法进行验证,发现该方法能够剔除信号中的周期噪声,并能够在很大程度上削弱有色噪声的影响。 2.针对陀螺信号中相关噪声的实际情况,首先利用小波变换削弱周期噪声以及部分白噪声的影响,然后建立了高阶AR模型,并将其应用到组合导航中,实测算例证明,该方法有效地提高了组合导航的精度。 3.在二位置寻北数据处理中,针对光纤陀螺信号中的趋势项,采用抗差估计拟合其系数再对其进行补偿;对信号中残留的噪声项以及干扰项,分别采用直接平均法、抗差估计法和小波滤波法进行处理,并对结果进行了分析和比较。 4.针对捷联惯导初始对准过程中Kalman滤波模型存在误差或系统噪声不能反映实际噪声的情况,提出利用具有反馈能力的Elman神经网络训练系统噪声方差阵,解决系统以及噪声的不确定性问题。 5.通过小波多分辨分析对陀螺仪和加速度计的输出信号进行消噪处理,然后由三参数序贯抗差估计解算初始姿态角,利用静基座下模拟数据对该方法进行验证,结果表明该方法能够保证捷联惯导在较短的时间内获得较高的对准精度。 6.在GPS/INS组合导航自适应滤波的基础之上,提出利用小波变换进行阈值消噪以提高组合导航精度。首先对惯性元件输出信号进行频谱分析,确定相应的多分辨分析尺度,以期对不同尺度下高频系数采取不同措施。然后对噪声占主要成分的尺度将其高频系数全部置零,对噪声和有用信号共同占有的尺度将其高频系数作阈值处理。利用实测数据进行验证,表明这种方法有效地削弱了惯性元件误差的影响,提高了GPS/INS组合导航系统的精度和可靠性。 7.针对GPS/DR组合导航Kalman滤波的异常扰动影响问题,引入了自适应滤波算法。给出了由预测残差确定自适应因子的过程。利用实测数据进行验证,表明无论是单因子自适应滤波还是多因子自适应滤波都能够很好地控制状态异常对滤波估值的影响,滤波精度均优于标准Kalman滤波导航解。The combination of the inertial navigation system and the satellite systems is one of the most important approaches that can improve the navigation precision and the reliability. However, the errors of inertia components are remarkable factors in influencing INS or GPS/INS. This dissertation mainly focuses on the foundation of the error models, the compensation of the errors and the controlling of the errors by using the methods of Robust Spectral Analysis, wavelet analysis, adaptive filtering and so on. Finally, the applications in FOG north determining, the initial alignment of SINS and the integrated navigation are introduced. The main works and contributions are summarized as follows: 1、In the FOG signals, there exists the low-frequency colored noise. First it is fitted and predicted for the initial signal. The robust spectral analysis is applied to differentiate the useful signal, colored noise and periodic noise from the initial signal. In simulating example, it is testified that this method can eliminate the periodic noise and degrade the influence of colored noise. 2、For the practical correlated noise in the gyro, periodic function fitting and wavelet transform are applied to degrade the periodic noise respectively. Then the higher-order AR models are introduced for the correlated noise fitting. Finally the two AR models are applied in GPS/INS navigation. And the result based on the wavelet transform and the higher-order AR model shows a major improvement in the precision of navigation. 3、Two-Position north determining data of FOG north seeker is analyzed and processed. Robust estimation is applied to calculate the coefficient of the trend part of the signal to reduce its influence. Average, robust estimation and wavelet transform are used and compared for the noise and the disturbance of the signal. 4、In Kalman filtering of SINS refined initial alignment, when the inaccurate model is constructed or the systematic covariance matrix is not consistent with the actual noise, it will degrade the filtering accuracy or even lead to radiation. In order to solve this problem, a new method based on Elman neural network and Kalman filtering is presented in this paper. First, the reliable state estimation of Kalman filtering for the known system is taken to train the Elman neural network. Then the trained neural network is applied to estimate the state parameters for the unknown system. By the simulating data, it is determined that this new algorithm can get over the shortcomings of Kalman filtering in SINS refined initial alignment. 5、A new method is presented to determine the initial attitude based on the wavelet transform and three-parameter sequential robust adjustment. First the wavelet multiresolution analysis is applied to de-noise the noise components from the measurements of gyros and accelerometers. Then the original attitude angles are calculated by the three-parameter sequential robust adjustment. By the simulating stationary data, it is determined that this new method can ensure high alignment accuracy in short time. 6、A new algorithm based on wavelet threshold de-noising for GPS/INS is presented to improve the precision of integrated navigation. First, frequency-spectral analysis for output signals of inertia elements is given to decide the scale of wavelet multiresolution analysis and the measures for their high frequency coefficients. The high frequency coefficients of the scale which mostly represents high frequent noise will be set zero and those of the scale which represents low frequent noise and useful signals will be dealt with by using the threshold value. By the calculation, it is shown that the new algorithm can effectively resist the influence of the errors of inertia elements and improve the precision of navigation. 7、An adaptive Kalman filtering is applied in GPS/DR integrated navigation to control the influences of outlying movement disturbances. The multi adaptive factors of the