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。     

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。     

智能农机中精准定位的研究与实现

对智能农机自动驾驶或辅助驾驶中定位方式进行改进,采用GPS/INS组合导航的方式实现精准定位。GPS/INS组合导航能克服仅采用GPS或INS进行定位的缺陷,实现优势互补,减少定位误差。研究了GPS/INS组合导航算法,并对GPS和INS数据采用卡尔曼滤波进行处理,从位置、速度、姿态三方面进行仿真对比分析,验证该算法确实能够减少定位误差,实现较精准的定位。     

INS/GPS/电子罗盘组合导航系统研究

根据船舶导航系统对导航精度的要求,利用联邦卡尔曼滤波技术,分别确立了INS/电子罗盘子滤波器和INS/GPS子滤波器的组合模式,设计了船舶INS/GPS/电子罗盘组合导航系统;仿真结果表明,将联邦卡尔曼滤波理论应用于INS/GPS/电子罗盘组合导航系统可以获得较为满意的导航精度.     

基于cubature Kalman filter的INS/GPS组合导航滤波算法

INS/GPS组合导航系统的本质是非线性的,为改善非线性下INS/GPS组合导航精度,提出将一种新的非线性滤波cubature Kalman filter（CKF）应用于INS/GPS组合导航中.为此,建立了基于平台失准角的非线性状态模型和以速度误差及位置误差描述的观测模型,分析了CKF滤波原理,设计了INS/GPS组合滤波器,对组合导航非线性模型进行了仿真.仿真结果显示,相对于扩展卡尔曼滤波（EKF）,CKF降低了姿态、位置和速度估计误差,CKF更适合于处理组合导航的状态估计问题.     

智能车辆中的INS/GPS组合导航技术

自主驾驶与辅助导航是目前智能汽车领域的一个热点.本文研究了一个由INS/GPS组合导航的智能车辆系统.该系统由GPS和INS组合实现,其核心算法是用卡尔曼滤波实现GPS和INS的数据融合.通过对INS的辅助,使这个组合导航系统具备容错能力,仿真结果表明,该组合系统满足定位和导航的功能.     

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失锁时利用神经网络对INS的导航误差进行修正,实现组合导航的连续性。跑车实验数据表明,GPS信号失锁360 s左右时,MLPNN辅助的组合导航方法最大位置误差在40 m以内,相对于纯惯性导航推算降低了35%的误差。该算法对GPS失锁后抑制GPS/INS组合导航系统误差快速发散、提高导航解算精度有显著效果。     

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Link 16与DGPS的组合导航系统

为进一步改善JTIDS/INS/GPS组合导航系统在现代联合作战中的作用,提出一种新的工作体制,即将差分GPS引入到JTIDS/INS/GPS组合导航系统中,可以显著提高导航定位精度.同时以Link 16的通信链路传输差分GPS的差分信息,节省系统资源,增强系统的可控性和抗干扰性.通过仿真对有无差分和差分基准站数量不同两种情况下的定位性能进行比较分析,验证了系统设计的有效性.     

基于ANFIS神经网络的GPS/INS组合导航信息融合

介绍了自适应神经网络模糊推理技术(ANFIS),在此基础上采取新息自适应调整的思想,设计了一种基于滤波器工作参数调整的GPS/INS组合导航神经网络辅助卡尔曼滤波器,利用神经网络的非线性,根据滤波器的实际输出在线实时动态调整滤波器参数,达到对滤波器的调整和控制。与传统卡尔曼滤波器进行计算机仿真比较表明,基于ANFIS神经网络的GPS/INS组合导航信息融合技术具有较强的自适应性,能够在复杂的环境下抑制数据的发散,提高导航精度。     

GPS/INS 超紧组合环路信息耦合模型及机理分析

在对不同GPS/INS超紧组合模型特点分析的基础上构建超紧组合中惯性与卫星环路信息耦合模型,提出了环路复制信号参量的外部控制方法,论证了超紧组合模型中环路信息与惯性导航结果的耦合机理.最后,进行了超紧组合耦合实验验证和分析,结果表明,超紧组合系统环路信号参量偏差与惯性状态误差间有着紧密的内在联系和深层次的耦合机理.     

Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation

The last two decades have shown an increasing trend in the use of positioning and navigation technologies in land vehicles. Most of the present navigation systems incorporate global positioning system (GPS) and inertial navigation system (INS), which are integrated using Kalman filtering (KF) to provide reliable positioning information. Due to several inadequacies related to KF-based INS/GPS integration, artificial intelligence (AI) methods have been recently suggested to replace KF. Various neural network and neuro-fuzzy methods for INS/GPS integration were introduced. However, these methods provided relatively poor positioning accuracy during long GPS outages. Moreover, the internal system parameters had to be tuned over time of the navigation mission to reach the desired positioning accuracy. In order to overcome these limitations, this study optimizes the AI-based INS/GPS integration schemes utilizing adaptive neuro-fuzzy inference system (ANFIS) by implementing, a temporal window-based cross-validation approach during the update procedure. The ANFIS-based system considers a non-overlap moving window instead of the commonly used sliding window approach. The proposed system is tested using differential GPS and navigational grade INS field test data obtained from a land vehicle experiment. The results showed that the proposed system is a reliable modeless system and platform independent module that requires no priori knowledge of the navigation equipment utilized. In addition, significant accuracy improvement was achieved during long GPS outages.     

Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation

Most of the present vehicular navigation systems rely on global positioning system (GPS) combined with inertial navigation system (INS) for reliable determination of the vehicle position and heading. Integrating both systems provide several advantages and eliminate their individual shortcomings. Kalman filter (KF) has been widely used to fuse data from both systems. However, KF-based integration techniques suffer from several limitations related to its immunity to noise, observability and the necessity of accurate stochastic models of sensor random errors. This article investigates the potential use of adaptive neuro-fuzzy inference system (ANFIS) for temporal integration of INS/GPS in vehicular navigation. An ANFIS-based module named “P–δP” is designed, developed, implemented and tested for fusing INS and GPS position information. The fusion process aims at providing continuous correction of INS position to prevent its long-term growth using GPS position updates. In addition, it provides reliable prediction of the vehicle position during GPS outages. The P–δP module was examined using real navigation system data compromising an Ashtech Z12 GPS receiver and a Honeywell LRF-III INS. The proposed module proved to be successful as a modeless and platform independent module that does not require a priori knowledge of the navigation equipment utilized. Limitations of the ANFIS module are also discussed.     

GPS信号自适应抗干扰系统

针对GPS卫星信号易受干扰,不稳定的问题,提出INS/GPS组合导航抗干扰的方法并用硬件电路进行实现验证。给出了惯性器件的误差模型,采用松散组合方式,设计卡尔曼滤波器,取姿态、速度、位置的误差作为状态变量。提出以INS与GPS输出的东北天向速度误差作为滤波器观测量的方案。通过计算机的仿真和实验验证,对系统的精度进行了分析,证明该方案是可行的,实现实时滤波计算,并能满足导航的精度要求。     

神经网络在MEMS-IMU/GPS组合导航中的应用研究

针对基于MEMS传感器组成的INS/GPS组合中GPS信号缺失的情况下,系统误差瞬时增大,滤波迅速退化无法继续工作的问题,本文提出利用神经网络辅助INS/GPS导航系统以解决这一问题的方法.该方法首先建立系统模型,用组合导航的输入作为网络模型的输入,通过网络训练得到输出需要参数,结合卡尔曼滤波用于组合导航以继续使导航系统工作,仿真结果表明该方法可行和有效性的.     

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.