基于MEMS技术的车载组合导航系统研究

针对当前车载GPS导航仪实时性和可靠性差的问题,设计出一种基于MEMS技术的低成本车载GPS/MIMU/GIS组合导航系统,建立了GPS/MIMU组合系统的误差模型,对该模型进行了计算机仿真研究,并运用地图匹配算法对GPS/MIMU/GIS导航信息误差进行修正;跑车试验表明,该组合导航系统成本低、精度高,可靠性强,特别适合于军用和民用车辆的导航定位。     

基于回算机制的GPS/INS时间延迟处理方法

针对组合导航中由于GPS时延而导致的定位精度下降的问题,提出了一种适用于低成本制导炸弹的时延处理方法.该方法在GPS数据输出延时过程中,利用预设存储器存储数据,基于回算机制完成GPS信息更新时刻的数据融合、GPS数据输出时刻的导航输出,减小时间不同步对组合导航数据融合的影响.该回算机制可控制计算量,不增加程序复杂性,适用于GPS数据丢失或异常等多种情况.针对回算机制提出了一种工程实践中的计算优化算法,在回算时取消卡尔曼滤波计算中的时间更新环节.该计算优化可节约回算过程的计算时间,避免整体数据延迟,同时不影响导航定位精度,可满足短时间内的低成本组合导航系统要求.靶试结果验证,时延处理方法及计算优化算法适用于低成本制导炸弹,具有一定的工程实用性.     

低成本SINS/GPS组合导航系统方案

低成本捷联惯性导航系统SINS、GPS硬件和相应的组合导航算法已经开始成熟,但仍然缺少简单可行的、完整的组合系统方案.针对低成本SINS\GPS组合导航设计了一套完整的方案.首先利用GPRS和TCP/IP通信链路实时传输GPS差分数据,提高GPS定位精度.用计算机串口接收SINS\GPS数据,并利用计算机时间使SINS和GPS数据同步.然后给出了SINS速度和位置更新的简化算法,由于低成本SINS无法确定航向角,所以使用SINS自带的姿态和航向参考系统输出的航姿信息.最后阐述了方案采用的组合导航数据融合卡尔曼滤波模型,并以RTK定位数据为参考真值进行了车载实验,实验表明组合系统更加稳健,定位精度明显提高.     

MEMS-IMU/GPS/GPRS组合导航方案设计与实现

针对日益增长的低成本导航定位需求,提出了一个MEMS-IMU/GPS/GPRS组合导航方案,以MEMS-IMU模块和SIM908单板机为载体,以GPRS为通讯手段,实时获取GPS/IMU位姿信息.并编写程序分别对GPS和IMU数据进行预处理并以松组合方式进行组合导航数据融合.为验证系统的可靠性和精度,进行了实际静态试验和车载实验,事后数据处理分析结果表明,提出的低成本组合导航系统具有一定的可靠性,导航定位精度达到分米级.     

BDS/SINS紧耦合组合导航技术仿真研究

研究北斗卫星导航系统定位精度优化问题.由于单一北斗系统不能满足飞行器导航要求,为了增强系统可靠性、实时性和抗干扰能力,针对卫星系统导航信号通视性差、易失锁和数据更新频率低等问题,提出了机载紧耦合BDS/SINS组合导航技术.对BDS和SINS的误差模型进行分析,构造了利用伪距和伪距率的状态方程和量测方程.仿真结果表明,紧耦合组合导航系统精度和可靠性更高,在少星条件下仍能满足载体导航需求,并弥补了BDS单一导航的不足.     

基于双DSP的SINS/GPS组合导航计算机系统

为满足SINS/GPS组合导航系统对实时性、精度的要求,设计了一种基于双DSP的高性能SINS/GPS组合导航计算机系统。本系统按功能划分为数据传输子系统和导航计算子系统。选用高性能数字信号处理器TMS320F28335(DSP1)和TMS320C6713(DSP2)分别作为数据传输子系统,导航计算子系统的核心处理器;两子系统之间通过双口RAM实现高速可靠通信。系统通过扩展外部接口实现对惯性组件数据,GPS数据实时采集,并输出导航参数,具有很强的实时性。     

高性能小型无人机导航计算平台设计

针对小型无人机长航时、高精度的导航要求,研究了一种低成本、高性能小型无人机组合导航系统。构建了由惯性元件、气压高度计和双天线GPS姿态测量系统构成的组合导航计算平台,采用了基于FPGA的SOPC技术,实现了系统功能的高度集成。针对信息融合算法中滤波延时较大的问题,采用了非对称双核的设计,在进行卡尔曼滤波的同时完成了捷联解算,大幅提高了导航信息的更新频率。     

GPS失效时组合导航系统修正方法研究

针对采用低成本惯性元件的MEMS-IMU/GPS组合导航系统在GPS信号长时间失效后系统趋于发散的问题,在研究捷联航姿解算算法、加速计测量姿态角算法和磁传感器解算航向角算法的基础上,提出将航姿辅助修正算法引入组合导航系统中,一旦系统检测到无效信号出现,则能自动切换到辅助修正状态,使导航结果被抑制在0.5°左右;通过半实物仿真实验,证明了该方法能够有效解决GPS信号长时间失效后系统发散问题,满足了在低成本MEMS惯性元件基础上的高精度导航要求。     

一种低成本GPS/DR组合导航系统设计

根据车载导航的特点,运用单自由度的加速度计代替里程计,提出一种基于低成本的单陀螺仪单加速度计的航位推算(DR)方案。航位推算短时精度高,但误差会随时间积累而变大,DR与GPS导航利用扩展型卡尔曼滤波,形成GPS/DR组合导航系统,融合2个系统的优势,完善导航能力。实际测试结果表明,该GPS/DR组合导航系统能满足车载导航的基本需求,在城市峡谷中有较高的定位精度,且在GPS信号遮挡时能保持定位。     

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.     

组合导航的AGV定位精度的改善

为了减小AGV惯性导航系统的累积误差,利用地埋标签辅助惯性导航系统构成一种自主式组合导航系统.在忽略陀螺仪误差的基础上,由惯性导航系统的运动方程推算出位置误差的计算式,得出影响误差的3个因素:加速度计偏差、AGV的行驶角度和地埋标签的数据更新时间.提出从影响误差的地埋标签数据更新时间入手,重新布放地埋标签以降低数据更新时间来提高AGV定位精度.通过Matlab对数据进行仿真,表明该方法能有效地降低AGV惯性导航系统的累积误差,进而改善了AGV的定位精度.     

PERFORMANCE ANALYSIS OF VARIOUS ARTIFICIAL INTELLIGENT NEURAL NETWORKS FOR GPS/INS INTEGRATION

An aircraft system mainly relies on a Global Positioning System (GPS) to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS absence because of ephemeric error, satellite clock error, multipath error, and signal jamming. To overcome these drawbacks, generally a GPS is integrated with an Inertial Navigation System (INS) mounted inside the vehicle to provide a reliable navigation solution. INS and GPS are commonly integrated using a Kalman filter (KF) to provide a robust navigation solution. In the KF approach, the error models of both INS and GPS are required; this leads to the complexity of the system. This research work presents new position update architecture (NPUA) which consists of various artificial intelligence neural networks (AINN) that integrate both GPS and INS to overcome the drawbacks of the Kalman filter. The various AINNs that include both static and dynamic networks described for the system are radial basis function neural network (RBFNN), backpropagation neural network (BPN), forward-only counter propagation neural network (FCPN), full counter propagation neural network (Full CPN), adaptive resonance theory-counter propagation neural network (ART-CPN), constructive neural network (CNN), higher-order neural networks (HONN), and input-delayed neural networks (IDNN) to predict the INS position error during GPS absence, resulting in different performances. The performances of the different AINNs are analyzed in terms of root mean square error (RMSE), performance index (PI), number of epochs, and execution time (ET).     

GPS误差的时间序列分析建模研究

定位精度是影响全球定位系统(GPS)应用的重要因素之一,目前采用的差分GPS虽然能够有效地提高定位精度,但属于非自主式定位方法,易被发现和攻击,限制了系统的应用范围;而组合导航定位系统结构复杂,实现成本较高。利用时间序列分析方法,分析GPS误差序列的统计特性,建立误差模型,可有效地改善GPS预报结果,提高定位精度。结合实际采样数据,给出了具体实现方法,实验结果表明了利用时间序列分析方法进行GPS数据建模分析的有效性及可行性。     

路网辅助的车载INS/GPS组合导航方法

针对车载INS/GPS组合导航系统在GPS无效时精度迅速下降的问题,提出了将车辆行驶的路网约束作为虚拟传感器,采用多传感器数据融合的方式,与INS和GPS组成INS/GPS/路网组合导航系统.当GPS失效时,使用路网辅助INS.仿真结果表明,在GPS无效时间段,该方法能有效减小系统定位误差.     

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.     

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.     

基于拓扑结构的地图匹配算法研究

随着GPS定位技术的日益推广,GPS技术被大量应用于各种导航系统中。但由于GPS系统存在一定的定位误差,那么如何向用户提供实时、准确的车辆位置信息成了导航系统的重点和难点。通过利用软件纠偏的方法对车辆的定位数据与电子地图数据进行配准纠正,即地图匹配,来减小电子地图的道路信息和GPS定位信息间的显示误差。该方法已应用于实际的工程应用,并取得了良好的效果。     

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...