车载INS/GPS组合导航系统建模与仿真

为了准确地对车辆进行定位,实现导航功能,将两种常用的导航定位技术GPS定位导航技术与惯性导航技术进行组合,介绍了INS/GPS组合导航系统的仿真方案。因车载组合导航系统对精度的要求不是很高,采用低成本的机械陀螺和加速度计作为惯性导航系统的测量器件。INS/GPS组合导航系统采用位置和速度组合模式,分别给出纯INS,纯GPS,组合导航系统的位置误差比较。结果分析表明,组合导航系统精度高于INS和GPS分别独立工作时的精度。     

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RAEKF的GPS/INS紧组合导航方法研究

针对GPS/INSS组合导航系统对高精度和高可靠性需求不断提升的现状,考虑载体运动复杂性导致运动过程中噪声数学统计特性无法精确确定,且滤波结果易受观测粗差影响的问题,提出一种抗差自适应扩展卡尔曼滤波(Robust Adaptive Extended Kalman Filter,RAEKF)方法。基于惯性导航、卫星导航原理与误差模型确定紧组合工作模式下的卡尔曼滤波数学模型;推导常用EKF模型并构造抗差自适应卡尔曼滤波,通过自适应调整观测值的使用权重提高滤波精度,结果表明抗差自适应滤波能有效抑制粗差对导航结果的影响。     

GPS/SINS/Odometer组合导航系统的研究

针对全球定位系统(GPS)和捷联惯性导航系统(SINS)组成的车载导航系统在GPS失效时精度会快速下降的问题,提出了GPS/SINS/Odometer(里程计)组合导航定位系统,采用联邦卡尔曼滤波对SINS进行误差估计并作反馈校正。当GPS无效时,就可以使用里程计辅助SINS。实验结果表明,在GPS失效的时间段内,该组合系统可以有效地抑制SINS的误差发散,并将其误差限制在较低的水平,保证了组合导航系统的精度与可靠性。     

GPS/INS超紧组合抗干扰性能分析

为提高GPS(全球定位系统)/INS(惯性导航系统)超紧组合系统的抗干扰性,分析了影响超紧组合系统抗干扰性的各种因素.基于传统PLL环路结构,对超紧组合系统建立了具体的数学模型,推导了超紧组合PLL环路的误差来源及每一项误差的计算公式,系统分析了影响超紧组合抗干扰性的因素,包括调整系数、码速率、动态性、相干积分时间、钟及环路带宽等,仿真分析了各种因素对超紧组合系统抗干扰性的影响,总结出提高超紧组合系统抗干扰性的方法.     

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.     

基于GPS/INS/磁力计多传感器融合的连续定位

许多场景都需要准确的连续定位,全球定位系统(GPS)能提供准确的定位、导航和时间服务,但其信号易受干扰,导致在隧道、地铁和地下停车场等环境中性能下降甚至无法使用的情况,提出了一种基于GPS/INS 磁力计多传感器融合的连续定位方法,在应用磁力计和惯性导航系统(INS)组合室内定位时,磁力计数据可对INS累积误差进行有效校正。室外用 GPS定位,当GPS信号较弱时,磁力计和INS的组合信号用于更新定位。为降低GPS信号噪声,采用卡尔曼滤波器对GPS和INS的组合信号进行滤波,针对磁力计易受环境干扰现象,提出一种利用磁力计动态计算和校正航向角的方法。测试结果表明,与未校正的航向角相比,多传感器融合的连续定位方法精度提高了60%,整体定位轨迹误差在2m以内。     

GPS/INS组合导航系统中的信息融合算法研究

该文以GPS／INS组合导航系统为应用背景,对系统的模型建立进行详细的介绍,针对当前应用于其中的Kalman滤波法,它对量测噪声模型变化不能很好进行最优估计的缺点,引入了模糊自适应理论,实时地在线修正新息协方差值,以改善滤波器的性能,提高组合导航系统精度,并且还介绍了野值检测和处理方法。最后进行实时仿真,经仿真结果表明该方法是有效的。     

GPS/INS组合导航系统设计及实现方案

将两种或多种导航系统(或传感器)组合起来成为一个综合系统,其导航性能比单一的系统好,因此组合导航系统目前已成为导航系统的发展方向之一。现代控制理论的成就,特别是最优估计理论的数据处理方法,为组合导航系统提供了理论基础。介绍了GPS/INS组合导航系统的经典组合方式和采用卡尔曼组合滤波器的组合方式。重点对采用卡尔曼滤波的软件组合方式进行分析和推导,将全组合滤波法和分布式组合滤波法进行了较全面的比较。     

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GPS/SINS紧耦合系统的新息外推法

在GPS/SINS紧耦合系统中,完好性是一个极其重要的指标。为缩短卫星故障检测时间,在新息检测法的基础上提出一种新息外推法,该算法通过对外推过程中产生的新息进行处理,形成检验统计量来检测卫星故障。结合GPS/SINS紧耦合系统,仿真结果表明,新息外推法比新息检测法能更快地检测慢变故障,且能在一定程度上削弱野值对故障检测的影响。     

Attitude estimation for accelerated vehicles using GPS/INS measurements

An advanced attitude observer for rigid bodies evolving in 3D-space using GPS-velocity and INS measurements has been proposed recently in the literature without specifying its domain of convergence and stability. With respect to conventional solutions, which do not involve linear velocity measurements, this observer is better adapted for vehicles subjected to important linear accelerations. The present paper proposes further modifications yielding two other attitude observers with rigorous semi-global convergence and stability results. Simulation results illustrate the compared performance of the three solutions and classical methods.     

Intelligently tuned wavelet parameters for GPS/INS error estimation

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of th...     

GPS/INS组合导航数据同步时标的计算方法研究

以GPS/INS组合导航系统为应用背景,针对GPS数据和惯性数据中时间同步的问题,提出了基于横摆角速度匹配、实时计算GPS延迟的方法,用于修正惯性数据与GPS的同步时标,提高了组合导航和车辆状态检测的精度。实验结果表明,计算得到的GPS延迟能够起到较好的修正效果。该方法仅利用导航系统数据,无需汽车内部参数和采集信号,具有自主性,有利于应用于外置检测和导航系统。     

Amplified wavelet-ANFIS-based model for GPS/INS integration to enhance vehicular navigation system

Inertial navigation system (INS) relying on gyroscopes and accelerometers has been recently utilized in land vehicles. These INS sensors are integrated with Global Positioning System (GPS) to provide reliable positioning solutions in case of GPS outages that commonly occur in urban canyons. The major inadequacies of INS navigation sensors are the high noise level and the large bias instabilities that are stochastic in nature. The effects of these inadequacies manifest themselves as large position errors during GPS outages. Wavelet analysis is a signal processing method which is recently auspicious by many researchers due to its advantageous adaptation to non-stationary signals and able to perform analysis in both time and frequency domain over other signal processing methods such as the fast Fourier transform in some fields. This research proposes the utilization of wavelet de-nosing to improve the signal-to-noise ratio of each of the INS sensors. In addition, a neuro-fuzzy module is used to provide a reliable prediction of the vehicle position during GPS outages. The results from a road test experiment show the effectiveness of the proposed wavelet—neuro-fuzzy module.     

A low-cost INS/GPS integration methodology based on random forest regression

This paper, for the first time, introduces a random forest regression based Inertial Navigation System (INS) and Global Positioning System (GPS) integration methodology to provide continuous, accurate and reliable navigation solution. Numerous techniques such as those based on Kalman filter (KF) and artificial intelligence approaches exist to fuse the INS and GPS data. The basic idea behind these fusion techniques is to model the INS error during GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby maintaining the continuity and improving the navigation solution accuracy. KF based approaches possess several inadequacies related to sensor error model, immunity to noise, and computational load. Alternatively, neural network (NN) proposed to overcome KF limitations works unsatisfactorily for low-cost INS, as they suffer from poor generalization capability due to the presence of high amount of noise.In this study, random forest regression has shown to effectively model the highly non-linear INS error due to its improved generalization capability. To evaluate the proposed method effectiveness in bridging the period of GPS outages, four simulated GPS outages are considered over a real field test data. The proposed methodology illustrates a significant reduction in the positional error by 24–56%.     

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。     

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.