受外界随机干扰影响的光纤陀螺信号分析与处理

针对受外界随机干扰影响的光纤陀螺信号进行分析并作了相应处理.分别比较了小波变换和建立在AR模型基础上的Kalman滤波两种滤波方法,并用实测数据进行验证.结果表明,相对于小波变换,建立在AR模型基础上的Kalman滤波不仅能够削弱有色噪声的影响,还能够部分削弱高频噪声的影响,滤波效果较好;将小波变换和Kalman滤波两种方法的优点结合起来,可以获得更好的滤波效果,有效地削弱了信号中各种干扰噪声的影响.     

受外界随机干扰影响的光纤陀螺信号分析与处理

针对受外界随机干扰影响的光纤陀螺信号进行分析并作了相应处理。分别比较了小波变换和建立在AR模型基础上的Kalman滤波两种滤波方法,并用实测数据进行验证。结果表明,相对于小波变换,建立在AR模型基础上的Kalman滤波不仅能够削弱有色噪声的影响,还能够部分削弱高频噪声的影响,滤波效果较好;将小波变换和Kalman滤波两种方法的优点结合起来,可以获得更好的滤波效果,有效地削弱了信号中各种干扰噪声的影响。     

利用牛顿插值的GPS/INS组合导航惯性动力学模型

针对GPS/INS组合导航中INS动力学建模不准确的问题,提出了一种基于牛顿插值的GPS/INS组合导航惯性动力学多阶建模算法。首先,介绍了牛顿插值的计算方法;然后,利用其对惯性系统Gauss-Markov模型进行改进,实现了多阶建模;最后,给出了组合导航详细的动力学模型和观测模型。利用实测数据对算法进行验证,并通过对比不同阶数建模方法寻求牛顿插值的最优建模阶数。结果表明,相比于传统GaussMarkov过程,基于牛顿插值的GPS/INS组合导航惯性动力学模型能够有效提高组合导航位置精度和姿态精度。与此同时,实验分析表明,4阶牛顿插值建模在实现模型准确性的前提下,降低了模型的复杂性,为牛顿插值建模阶数选取提供了良好的借鉴。     

基于小波阈值消噪自适应滤波的GPS/INS组合导航

惯导系统中惯性元件误差是影响惯性导航及其组合导航精度的重要因素.在GPS/INS组合导航自适应滤波的基础之上,提出利用小波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及不同尺度下对高频系数采取的相应措施.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,结果表明这种方法能够有效地削弱惯性元件误差的影响,提高GPS/INS组合导航系统的精度和可靠性.     

GPS/INS松组合与紧组合的实现与定位精度比较

GPS/INS组合导航技术包括两种组合模式:松组合与紧组合.在此分别给出了两种组合模式的算法,编程实现了GPS/INS松组合、紧组合两种组合导航的算法,并在此基础上根据车载组合导航的实测数据,对定位精度进行了比较分析,得出紧组合相比于松组合能够获得更好的定位精度.     

改进的UKF在GPS/INS组合导航中的应用

在GPS/INS组合导航中,传统UKF(Unscented Kalman Filter)计算量大,无法满足实时性要求。而且当动力学模型受到异常扰动误差影响时,其精度与稳定性易受到影响。针对以上问题,利用最小偏度单形采样策略降低UKF计算量以提高精度;通过自适应调整过程噪声以降低动态异常扰动误差对UKF精度与稳定性的影响。由此提出了一种改进UKF算法,用于GPS/INS组合导航。仿真实验结果表明,改进UKF算法用于GPS/INS组合导航的精度要优于UKF算法。     

改进的UKF在GPS/INS组合导航中的应用

在GPS/INS组合导航中,传统UKF(Unscented Kalman Filter)计算量大,无法满足实时性要求。而且当动力学模型受到异常扰动误差影响时,其精度与稳定性易受到影响。针对以上问题,利用最小偏度单形采样策略降低UKF计算量以提高精度;通过自适应调整过程噪声以降低动态异常扰动误差对UKF精度与稳定性的影响。由此提出了一种改进UKF算法,用于GPS/INS组合导航。仿真实验结果表明,改进UKF算法用于GPS/INS组合导航的精度要优于UKF算法。     

基于低通滤波的GPS/INS组合导航模型研究

惯导系统中惯性元件误差是影响惯性导航及组合导航精度的重要因素。本文首先分析了INS加速度计及陀螺仪在不同方向上的原始观测数据误差源及其相关作用,并给出低通滤波及GPS/INS(Global Positioning System/Inertial Navi-gation System)组合导航模型。在此基础上,提出采用低通滤波器消除INS原始数据中的高频随机误差,提高GPS/INS组合导航精度的技术路线。模拟INS的线速度及角加速度原始观测数据,对本文模型进行INS自主导航测试,导航精度得到明显提高。进一步采用实测数据进行组合导航分析,滤波前后计算的位置误差比较可以看出滤波后的导航解要优于滤波前,X、Y、Z三个方向上导航精度分别提高了15.1%、28.3%、28.1%。在残差最大值的比较上,三个方向上都有所减小,说明本文模型可有效提高导航精度。     

顾及有色噪声的光纤陀螺信号的抗差谱分析

在惯导系统中，对光纤陀螺仪的零位漂移一般采取滤波的方法对其进行补偿或削弱。本文针对信号中含有低频有色噪声，在Kalman滤波和小波变换消噪不理想的情况下，提出一种新的处理零位漂移的方法。首先对信号中的有色噪声进行拟合和预报，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分离开。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，并能够削弱有色噪声的影响。     

基于二次曲面的周期拟合GPS高程模型的研究

为了改善GPS大地高向正常高转换的精度,提出了基于二次曲面的周期拟合解算GPS高程异常的新思路.首先用二次曲面函数拟合消除趋势项,然后通过离散傅立叶变换提取频率信息,从而获得各种不同周期函数,高程异常值可以通过各种不同周期函数的叠加未获得.实际数据表明该方法不仅计算简单而且较常规的拟合模型有较好的应用价值.     

一种低动态高抖动环境下的GPS/INS紧组合方法

针对低动态高抖动环境下,影响GPS/INS紧组合精度的重要因素——惯性测量单元(IMU)数据中的噪声,该文提出利用小波降噪方法分离IMU数据中的噪声和有用信号以提高GPS/INS紧组合的精度。首先对IMU数据进行小波分解后得到的高频系数进行阈值量化处理,然后将GPS观测数据与降噪后的IMU数据进行GPS/INS紧组合解算,最终得到载体的导航信息。实例结果表明,该方法可以大幅提升GPS/INS紧组合的精度和稳定可靠性。     

A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications

The combined navigation system consisting of both global positioning system (GPS) and inertial navigation system (INS) results in reliable, accurate, and continuous navigation capability when compared to either a GPS or an INS stand-alone system. To improve the overall performance of low-cost micro-electro-mechanical systems (MEMS)-based INS/GPS by considering a high level of stochastic noise on low-cost MEMS-based inertial sensors, a highly complex problems with noisy real data, a high-speed vehicle, and GPS signal outage during our experiments, we suggest two approaches at different steps: (1) improving the signal-to-noise ratio of the inertial sensor measurements and attenuating high-frequency noise using the discrete wavelet transform technique before data fusion while preserving important information like the vehicle motion information and (2) enhancing the positioning accuracy and speed by an extreme learning machine (ELM) which has the characteristics of quick learning speed and impressive generalization performance. We present a single-hidden layer feedforward neural network which is employed to optimize the estimation accuracy and speed by minimizing the error, especially in the high-speed vehicle and real-time implementation applications. To validate the performance of our proposed method, the results are compared with an adaptive neuro-fuzzy inference system (ANFIS) and an extended Kalman filter (EKF) method. The achieved accuracies are discussed. The results suggest a promising and superior prospect for ELM in the field of positioning for low-cost MEMS-based inertial sensors in the absence of GPS signal, as it outperforms ANFIS and EKF by approximately 50 and 70%, respectively.     

A combined algorithm of improving INS error modeling and sensor measurements for accurate INS/GPS navigation

Although the integrated system of a differential global positioning system (DGPS) and an inertial navigation system (INS) had been widely used in many geodetic navigation applications, it has sometimes a major limitation. This limitation is associated with the frequent occurrence of DGPS outages caused by GPS signal blockages in certain situations (urban areas, high trees, tunnels, etc.). In the standard mechanization of INS/DGPS navigation, the DGPS is used for positioning while the INS is used for attitude determination. In case of GPS signal blockages, positioning is provided using the INS instead of the GPS until satellite signals are obtained again with sufficient accuracy. Since the INS has a very short-time accuracy, the accuracy of the provided INS navigation parameters during these periods decreases with time. However, the obtained accuracy in these cases is totally dependent on the INS error model and on the quality of the INS sensor data. Therefore, enhanced navigation parameters could be obtained during DGPS outages if better inertial error models are implemented and better quality inertial measurements are used. In this paper, it will be shown that better INS error models are obtained using autoregressive processes for modeling inertial sensor errors instead of Gauss–Markov processes that are implemented in most of the current inertial systems and, on the other hand, that the quality of inertial data is improved using wavelet multi-resolution techniques. The above two methods are discussed and then a combined algorithm of both techniques is applied. The performance of each method as well as of the combined algorithm is analyzed using land-vehicle INS/DGPS data with induced DGPS outage periods. In addition to the considerable navigation accuracy improvement obtained from each single method, the results showed that the combined algorithm is better than both methods by more than 30%.     

顾及动力学模型误差影响的GPS/INS组合导航自适应滤波算法

在顾及动力学模型随机误差的情况下,设计了一种GPS/INS自适应滤波算法.针对BP神经网络存在的训练速度慢、易陷入局部极小值等问题,对神经网络学习算法进行了改进.利用神经网络进一步减小系统误差对导航解的影响,给出了顾及动力学模型随机误差和系统误差的GPS/INS自适应滤波算法,并利用实测数据验证了算法的有效性.     

基于双差伪距/伪距率的GPS/SINS紧组合导航

GPS/SINS组合导航系统的观测模型直接关系着导航系统的精度。提出了一种基于双差伪距/伪距率的GPS/SINS紧组合观测模型。分析了采用双差伪距和采用双差伪距/伪距率两种观测模型对组合导航输出参数精度的影响。实测数据结果表明,采用双差伪距和采用双差伪距/伪距率作为观测值均能实现组合导航系统的收敛。引入双差伪距率观测值明显改善了系统的可观测性,不仅提高了组合导航中速度和姿态角的估计精度,也加快了速度误差和姿态角误差估计的收敛速度。     

一种小波与谱分析结合的GPS精密卫星钟差拟合方法研究

根据卫星钟固有的特性,即频移、频漂和频漂率以及周期性,提出一种新的拟合方法。新方法首先构造一个合理的钟差模型,该模型包含一个二次多项式和多个周期项。然后利用IGS提供的钟差产品数据,先粗略补充缺失数据,用二次多项式拟合;并对拟合后的残差进行小波分析,作降噪处理;最后通过谱分析,确定其主要的周期项,从而构建出适当的拟合模型,实现精密GPS卫星钟差拟合和预报。多天数据的实验结果表明,采用本文提出的新方法能够有效地对卫星钟差进行拟合和预报,满足不同目的的需求。     

Klobuchar模型和NeQuick模型在中国地区的精度评估

将CODE以及GPS广播星历提供的8个系数分别作为Klobuchar模型的输入参数,利用Klobuchar模型及NeQuick模型计算得到中国地壳运动观测网15个GPS基准站上2000～2008年的电离层VTEC序列,以欧洲定轨中心CODE提供的事后电离层产品作为参考标准,得到了两个模型在中国地区的精度评估结果。     

Design of minimax robust filtering for an integrated GPS/INS system

The problem of navigation systems with uncertain noise is considered. A minimax robust filtering which can minimize the worst performance under noise uncertainties using the game theory is proposed. This new filter is applied to an integrated GPS/INS navigation system. A high dynamics aircraft trajectory is designed to test the new filter. The results show that minimax robust filtering performs better than standard Kalman filtering when noise parameters of an inertial measurement unit change their statistical properties. Received: 21 October 1997 / Accepted: 26 May 1999