UKF在INS/GPS直接法卡尔曼滤波中的应用

提出将Unscented卡尔曼滤波(UKF)用于INS/GPS组合导航系统的直接法卡尔曼滤波,避免了对非线性的系统状态方程进行线性化.以INS输出的导航参数及平台误差角等作为系统状态,惯导力学编排方程和姿态误差方程作为系统状态方程,GPS输出的导航参数作为量测,采用UKF方法对系统导航参数直接进行估计.仿真结果表明,UKF方法有效地解决了直接法卡尔曼滤波中系统状态方程的非线性问题,并使INS/GPS组合导航系统具有较高的导航定位精度.     

基于结构分解的GPS/INS组合导航粒子滤波技术

针对粒子滤波应用于GPS/INS组合导航系统时难以保证滤波实时性的问题,提出一种基于线性/非线性结构分解的改进粒子滤波算法.改进算法对状态方程进行线性/非线性结构分解,分别采用重点采样和线性卡尔曼方式进行一步预测递推,充分发挥粒子滤波和卡尔曼滤波的特点,有效降低了粒子滤波的计算量,在保证GPS/INS组合导航系统滤波精度的条件下提高了组合滤波的实时性.     

基于因子图算法的INS/GPS信息滞后处理方法

针对全球定位系统（GPS）信息滞后导致惯性导航系统（INS）/GPS组合导航系统实时性差的问题,利用因子图算法可以在一个信息融合时刻处理各信息源不同时刻量测信息的特点,提出了一种INS/GPS信息滞后处理方法。在系统接收到GPS信息之前,因子图模型中只添加关于INS信息的因子节点,经增量推理求出组合导航结果,保证系统的实时性。待系统接收到GPS信息之后,再将关于GPS信息的因子节点添加到因子图模型中,修正INS误差,从而保证系统长时间高精度运行。仿真结果表明,当上一时刻实时导航状态量对INS误差修正效果随GPS信息滞后时间变长而逐渐变差时,可以采用上一时刻刚刚完成量测更新的导航状态量实现INS误差的有效修正。因子图算法在保证系统精度的前提下,避免了GPS信息滞后对INS/GPS组合导航系统实时性的不良影响。     

基于MLP神经网络改进组合导航算法

为解决GPS信号失锁条件下,GPS/INS(inertial navigation system)组合导航系统解算精度降低甚至发散的问题,提出采用多层感知机神经网络(multilayer perceptron neural networks,MLPNN)来辅助组合导航系统.在GPS信号有效时对神经网络进行训练,在GPS...     

Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration

Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.     

GPS/INS组合导航系统时间同步系统设计

为了说明高动态环境中时间同步对于组合导航系统的重要性,在Kalman滤波方程的基础上,推导了时间同步误差与Kalman滤波结果之间的定性关系.提出一种利用GPS接收机中1PPS(Pulse Per Second)信号作为同步标签的时间同步方法,将IMU中的数据加上精确的时间标签,从而达到时间同步的目的.全部时间同步功能由FPGA实现,利用Verilog HDL语言进行开发,整体硬件结构简单而且适用范围广.试验结果显示了这种时间同步设计可以明显减小滤波结果的估计误差,有效的提高了组合导航系统的定位精度.     

小波变换在车辆GPS导航的应用

在分析车辆GPS导航信号观测粗差、噪声特性的基础上,提出了基于小波变换的车辆GPS导航信号粗差探测及滤波算法.数据仿真表明:该算法能够有效地克服Kalman滤波需要精确数学模型和滤波效果受粗差影响较大的缺点.     

基于弹载IMU/GPS组合导航系统的动基座对准研究与仿真

对基于弹载IMU／GPS组合导航系统的动基座对准问题进行了研究与仿真;首先,分析了弹载IMU与GPS的系统误差,建市获得了其系统误差模型;然后,利用卡尔曼滤波技术,设计了弹载IMU／GPS组合导航系统的动基座对准算法;仿真结果表明,在初始误差较大的情况下,经过360秒的动基座对准,IMU的姿态角误差可降至10个角秒,同时位置和速度误差也得到了有效修正,从而证明该动基座对准算法是行之有效的。     

基于GPS与全站仪的惯性导航部件安装校准技术

针对惯性导航系统的初始安装误差无法消除,且该误差会严重影响试验机的惯性导航系统导航精度的问题,研究了一项结合测绘型GPS和全站仪测量的惯性导航部件安装校准技术,通过GPS定位和全站仪测量并解算出试验机航向和真北方向之间的夹角,与试验机上惯导系统指示数据进行对比,从而达到快速准确校准惯导系统安装误差的目的.运用该技术进行...     

Enhancing positioning accuracy of GPS/INS system during GPS outages utilizing artificial neural network

Integrated global positioning system and inertial navigation system (GPS/INS) have been extensively employed for navigation purposes. However, low-grade GPS/INS systems generate erroneous navigation solutions in the absence of GPS signals and drift very fast. We propose in this paper a novel method to integrate a low-grade GPS/INS with an artificial neural network (ANN) structure. Our method is based on updating the INS in a Kalman filter structure using ANN during GPS outages. This study focuses on the design, implementation and integration of such an ANN employing an optimum multilayer perceptron (MLP) structure with relevant number of layers/perceptrons and an appropriate learning. As a result, a land test is conducted with the proposed ANN + GPS/INS system and we here provide the system performance with the land trials.     

组合导航计算系统的Simulink仿真

采用联邦卡尔曼滤波算法,分析了INS/GPS/ADS/CNS组合导航计算系统的数学模型,然后以MATLAB/Simulink为平台,构建了INS/GPS/ADS/CNS组合导航计算系统的仿真模型并进行了仿真。结果表明:建立的仿真模型能正确仿真组合导航计算系统的工作过程,并且具有良好的可视化效果,为组合导航系统的研究提供了有效的工具。     

惯性导航系统在线标定方法研究

论文针对惯性导航系统在线标定的问题,利用GPS提供的基准速度和位置信息,采用基于卡尔曼滤波技术的“速度＋位置”匹配方法,以惯导系统与GPS的速度和位置的差值作为量测值,对加速度计和陀螺仪误差进行在线标定。仿真结果表明,经在线标定补偿后惯导系统定位误差降低了97．2％。该方法显著提高了导航精度,效率较高,具有一定的实用价值。     

基于神经计算的GPS／SINS组合导航滤波器设计

将线性二次型最优化方法与Ｈｏｐｆｉｅｌｄ神经网络相结合，研究了一种基于神经计算的滤波算法。而且，将算法应用于全球定位系统（ＧＰＳ）与捷联惯导系统（ＳＩＮＳ）的组合导航，通过计算机仿真，与卡尔曼滤波算法结果进行了比较，说明了该滤波算法的有效性和实用性。     

Multisensor 3D Posture Determination of a Mobile Robot Using Inertial and Ultrasonic Sensors

This paper presents methodologies and techniques for fusing inertial and ultrasonic sensors to estimate the current posture of a mobile robot navigating over indoor uneven terrain. This new type of pose tracking system is developed by means of fusing an inertial navigation subsystem (INS) and an ultrasonic localization subsystem. Extended Kalman filtering (EKF)-based algorithm for integrating both the subsystems is proposed to obtain reliable attitude and position estimates of the vehicle and to eliminate the accumulation errors caused by wheel slippage and surface roughness. Experimental results are conducted to illustrate feasibility and effectiveness of the proposed system and method.     

The integration of strap-down INS and GPS based on adaptive error damping

The concept and results of integration of a strap-down inertial navigation system (INS) based on low-accuracy inertial sensors and the global positioning system (GPS) have been presented in this paper. This system is aimed for the purposes of navigation, automatic control, and remote tracking of land vehicles. The integration is made by the implementation of an extended Kalman filter (EKF) scheme for both the initial alignment and navigation phases. Traditional integration schemes (centralized and cascaded) are dominantly based on the usage of high-accuracy inertial sensors. The idea behind the suggested algorithm is to use low-accuracy inertial sensors and the GPS as the main source of navigation information, while the acceptable accuracy of INS is achieved by the proper damping of INS errors. The main advantage of integration consists in the availability of reliable navigation parameters during the intervals of absence of GPS data. The influence of INS error damping coefficients is different depending on the fact whether the moving object is maneuvering or is moving with a constant velocity at that time. It is proposed that INS error damping gain coefficients generally should take higher values always when GPS data are absent, while at the same time their values in the error model (EKF prediction phase) can be additionally adapted according to the actual values of vehicle acceleration. The analysis of integrated navigation system performances is made experimentally. The data are acquired along the real land vehicle’s trajectory while the intervals of absence of GPS data are introduced artificially on the parts characterized both by maneuver and by constant velocity.     

一种模糊QR滤波及在组合导航中的仿真研究

总结了常用的自适应滤波的方法，并提出了一种基于模糊逻辑的自适应卡尔曼滤波技术，用模糊逻辑自适应推理器来“在线”修正卡尔曼滤波系统噪声协方差Q和测量噪声协方差R，从而使滤波器不断执行最优估计。仿真结果表明该方法可以提高GPS／INS组合导航系统的精度和可靠性。     

INS/GPS组合导航系统卡尔曼滤波的并行实现*

本文采用并行处理技术对ＩＮＳ／ＧＰＳ组合导航系统中卡尔曼滤波的实时性问题进行了研究。     

基于CenSurE特征的SAR/INS组合导航景象匹配算法

在SAR／INS组合导航系统中,所获取的SAR图像可能存在严重的斑点噪声和几何变形,对此,提出一种基于CenSurE特征的SAR／INS组合导航用景象匹配算法．该算法针对惯性组合导航的工作特点进行设计．首先提取CenSurE特征和垂直的SURF描述符,利用夹角余弦相似度量方法进行特征匹配;然后,采用分组一致采样算法和最小二乘精确匹配算法获取高精度的航向和位置偏差信息．景象匹配性能评价实验表明。在匹配适应性、匹配速度、精度和鲁棒性等方面,CenSurE特征都很优越,可以满足SAR／INS景象匹配导航系统匹配修正的高性能要求．     

惯导/里程仪组合导航系统算法研究

惯导系统误差随着导航时间而增长,而里程仪测量误差一般随着运载体行驶距离而增加,惯导系统和里程仪具有互补性,推导了简化的惯导/里程仪组合导航系统的卡尔曼滤波方程,研究了里程仪刻度系数误差校正的方法,并提出了里程仪打滑和滑行故障的判断准则,仿真结果表明：经过里程仪刻度系数校正后,组合导航系统能有效提高定位精度,并且具备一定的容错能力.     

自适应卡尔曼滤波在组合导航中的应用研究

对惯性导航系统（INS）与全球导航系统（GPS）分别进行了具体探讨,对比了两者的优缺点,针对INS／GPS组合导航系统中由于模型不准或因量测噪声的复杂多变造成的发散问题,引入了一种基于输出相关法的自适应卡尔曼滤波技术。通过在自适应滤波算法中推算最优稳态增益来调整量测噪声,抑制滤波器的发散,为GPS／INS组合导航系统实现高精度导航提供了有效的途径。仿真结果表明该算法能很好地对系统状态进行最优估计并适应系统噪声的变化,具有比常规卡尔曼滤波更高的导航精度。