静电陀螺漂移的补偿和监控方法

简要介绍静电陀螺惯性导航系统的基本原理及其常平架结构。由于静电陀螺具有良好的漂移特性,上述系统位置误差随时间增长速度极低。着重讨论静电陀螺漂移模型及其在系统中补偿和监控方法。利用系统内部冗余轴漂移的实时测量将卡尔曼滤波器应用于上述系统,有效地改善了惯性导航系统的精度;此外还同时估计出绕冗余轴方向陀螺漂移的未知常值偏差,取得满意的仿真结果。     

一种新的激光捷联惯导系统级温度补偿方法

激光捷联惯导工作时,惯组内的温度会随着时间不断升高,引起惯性器件标度因数和零偏的变化,从而无法满足惯组在全温范围内工作.因此,有必要采取相应措施来减少温度带来的误差.提出一种通过3次样条插值法建立初始模型,不断迭代计算模型偏差修正样条曲线的方法,确立激光陀螺和石英挠性加速度计的温度误差模型存入DSP 中,最终由导航计算机实现惯组输出的实时补偿.通过标定和静态通电验证了模型的正确性和重复性,为进一步提高惯导精度奠定了基础.     

一种全天时星跟踪器相对惯导的安装阵在线快速估计方法

全天时星跟踪器与捷联惯导组成的惯性/天文组合导航系统具有精度高、自主性强等优点，在飞机、无人机、船舶等领域具有广泛的应用前景。组合导航系统长时间工作中力热变化导致安装矩阵漂移进而影响导航系统精度，全天时星跟踪器由于天空光影响视场小，一次只能观测一颗恒星，无法根据一副星图直接进行安装阵估计。提出了一种基于Levenberg-Marquart （L-M）算法的捷联惯性/天文组合导航系统安装阵在线快速高精度估计方法，利用捷联惯导姿态测量值，将不同时刻的观测星矢量转移到同一时刻同一坐标系中，构造多颗观测星的观测矢量误差与导航星矢量最小二乘目标函数，利用自适应步长的L-M算法对其进行迭代求解，实时得到系统安装矩阵的变化量。试验结果表明，使用该方法后星跟踪器在捷联惯导本体坐标系的输出恒星投影矢量精度提升了1倍以上，在线估计时间优于5 ms，满足用户实时性和高精度要求。     

双轴惯导轴系非正交误差全空间补偿技术研究

旋转调制技术实现了捷联惯导的高精度长航时导航,但轴系非正交误差的存在影响着导航姿态精度。传统轴系非正交误差补偿方法是针对旋转轴停留在固定位置完成的,提出一种全空间的轴系非正交误差补偿方法,不限定旋转轴的转停位置。试验结果证明该误差补偿方法较传统方法更优,对惯导姿态精度提升明显。     

减小双轴旋转惯导中陀螺仪随机漂移影响的方法研究

针对双轴旋转惯导惯性器件的随机误差无法在导航过程中自动进行补偿的问题,提出优化的两位置重调(TPR)的方法来补偿系统的随机误差导致的方位和位置误差,以提高双轴旋转惯导的长航时精度.与传统两位置重调(CPR)方法相比,使用优化的误差传递方程的两位置重调的方法,可以在少于6h条件下估计出系统的方位误差,从而使得系统的位置精度和方位精度都得以极大的提高.根据惯性器件的随机误差导致的方位误差的特性,建立了TPR的误差模型.通过仿真,证明了该方法的有效性.     

SINS/ANS integration for augmented performance navigation solution using unscented Kalman filtering

The fundamental concept of the multisensor integrated navigation system is the utilization of a medium precision INS in conjunction with one or more auxiliary sensors which perform as error bounding sources. Strapdown inertial navigation system (SINS) integrated with astronavigation system (ANS) yields reliable mission capability and enhanced navigational accuracy for spacecrafts. The theory and characteristics of integrated system based on unscented Kalman filtering is investigated in this paper. This Kalman filter structure uses unscented transform to approximate the result of applying a specified nonlinear transformation to a given mean and covariance estimate. The filter implementation subsumed here is in a direct feedback mode. Axes misalignment angles of the SINS are observation to the filter. A simple approach for simulation of axes misalignments using stars observation is presented. The SINS error model required for the filtering algorithm is derived in space-stabilized mechanization. Simulation results of the integrated navigation system using a medium accuracy SINS demonstrates the validity of this method on improving the navigation system accuracy with the estimation and compensation for gyros drift, and the position and velocity errors that occur due to the axes misalignments.     

低成本捷联惯导不对称动态误差的神经网络补偿

 针对低成本捷联惯导系统(SINS)中陀螺动态误差的不对称性在角振动条件下造成姿态漂移的问题,设计了多层前向神经网络的补偿模型。在标定模型参数时,为降低对外部参考信号测量精度的要求,提出用姿态解算的最终误差作为网络优化目标的训练方法。由于最终的姿态误差不是网络的期望输出,无法采用有导师的训练方法,为此采用了微粒群优化算法。仿真实验结果表明:补偿后的陀螺动态误差的不对称度减小了一个数量级。     

基于多参量模型的光纤陀螺温度误差补偿

温度漂移误差是制约光纤陀螺精度的重要因素之一。针对传统光纤陀螺温度补偿方法仅对温度项建模导致补偿精度差的问题,提出了一种新型多参量模型来补偿光纤陀螺温度误差的方法。通过对陀螺零漂误差和温度各相关项进行相关性分析,将温度和温度速率的乘积项及温度梯度滞后项引入到温度漂移误差模型中,建立了多参量分段补偿模型对零偏进行补偿,显著改善了光纤陀螺的零偏稳定性。使用实测光纤陀螺数据对提出的补偿方法进行实验验证,结果表明采用该方法补偿后,零偏误差平方和降低2个数量级,陀螺漂移均值、方差稳定在零点附近,补偿效果优于温度项分段拟合方法,与非线性模型预测效果相当。     

旋转惯导中加速度计尺寸效应误差分析及补偿

 对旋转调制惯导转位过程中系统精度受加速度计尺寸效应影响的问题进行了研究。通过分析尺寸效应引起惯导系统(INS)导航误差的机理,推导了尺寸效应作用下加速度测量误差与速度误差的解析表达式。根据理论分析结果,分别给出了以速度误差和加速度误差作为观测量进行尺寸参数辨识的方法,并进行了必要讨论。结合旋转惯导转位过程中的速度误差特征,选取加速度测量误差为观测量对尺寸参数进行试验标定,尺寸参数辨识精度优于1.29 mm(1σ)。在此基础上给出了尺寸效应误差补偿方法,并在不同初始方位下对补偿效果进行试验验证,结果表明,通过尺寸效应补偿可以有效提高旋转惯导转位过程中的速度精度。     

Integration of GPS and low cost INS for pedestrian navigation aided by building layout

In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pedestrian navigation for these environments should be realized by the integration of GPS and inertial navigation system （INS）. However, the lowcost INS could induce errors that may result in a large position drift. The problem can be minimized by mounting the sensors on the pedestrian＇s foot, using zero velocity update （ZUPT） method with the standard navigation algorithm to restrict the error growth. However, heading drift still remains despite using ZUPT measurements since the heading error is unobservable. Also, tbot mounted INS suffers from the initialization ambiguity of position and heading from GPS. In this paper, a novel algorithm is developed to mitigate the heading drift problem when using ZUPT. The method uses building lay- out to aid the heading measurement in Kalman filter, and it could also be combined for the initial- ization. The algorithm has been investigated with real field trials using the low cost Microstrain 3DM-GX3-25 inertial sensor, a Leica GS10 GPS receiver and a uBlox EVK-6T GPS receiver. It could be concluded that the proposed method offers a significant improvement in position accuracy for the long period, allowing pedestrian navigation for nearly40 min with mean position error less than 2.8 m. This method also has a considerable effect on the accuracy of the initialization.     

Online INS/GPS integration with a radial basis function neural network

Most of the present navigation systems rely on Kalman filtering to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In general, INS/GPS integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and growth of position errors with time for INS. Present Kalman filtering INS/GPS integration techniques have some inadequacies related to the stochastic error models of inertial sensors, immunity to noise, and observability. This paper aims to introduce a multi-sensor system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN). A multi-layer perceptron ANN has been recently suggested to fuse data from INS and differential GPS (DGPS). Although being able to improve the positioning accuracy, the complexity associated with both the architecture of multi-layer perceptron networks and its online training algorithms limit the real-time capabilities of this technique. This article, therefore, suggests the use of an alternative ANN architecture. This architecture is based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks. The INS and GPS data are first processed using wavelet multi-resolution analysis (WRMA) before being applied to the RBF network. The WMRA is used to compare the INS and GPS position outputs at different resolution levels. The RBF-ANN module is then trained to predict the INS position errors and provide accurate positioning of the moving platform. Field-test results have demonstrated that substantial improvement in INS/GPS positioning accuracy could be obtained by applying the combined WRMA and RBF-ANN modules.     

极区格网阻尼导航方法

针对舰艇在极区航行时,其常用惯性导航系统机械编排存在精度下降、无北向基准等问题,设计了适用于极区工作的格网坐标系捷联惯性导航系统机械编排.在构建了格网坐标系参考框架的基础上,建立了格网坐标系捷联惯导系统的机械编排,并分析了其误差传播特性.通过误差分析,确定了格网坐标系捷联惯导系统中存在的周期性振荡,并提出了适用于格网坐标系捷联惯导系统的阻尼技术,有效抑制了周期性振荡.最后,通过仿真实验验证了该系统在极区工作的可行性和阻尼技术的有效性.     

弹道导弹天文/惯性导航误差修正方法研究

通过分析导弹天文导航和惯性导航的导航原理和误差模型,在基于充分利用高精度天文导航参数的思想上,提出了组合导航的误差修正方法。首先对两种导航参数进行了归算,而后提出了利用多次的天文测量参数对惯性测量误差进行修正的方法。该方法能较为准确地分离出引起射程误差较大的关机点速度、位置等的误差值,从而为实现利用天文导航进行射程误差修正提供前提条件。仿真计算表明,该方法是行之有效的。     

基于相对导航的多平台INS误差联合修正方法

在机群协同编队飞行中,编队成员仅装载惯性导航系统(INS)的方式具有隐蔽、抗干扰等明显的优势,但随着航时的增加,INS误差将不断累积.使导航系统失效,为此,提出基于相对导航的多平台INS误差联合修正方法.首先,建立了编队成员相对导航运动模型及非线性观测模型,采用量测重构技术构造了伪线性观测模型并推导了观测噪声协方差矩阵...     

基于改进的磁场检验和主方向算法的行人航向估计

针对以惯导为核心的行人定位算法存在航向角失真的问题，提出了一种融合惯性测量单元(IMU)和磁力计的行人导航算法，利用步幅航向的变化量将行人的运动分为直线行走和曲线行走两部分，当检测到行人直线行走时，提出一种实时建立主方向的自适应航向角漂移修正算法，对航行角误差进行补偿；当行人曲线行走时，辅助地磁场信息，使用磁场强度、地磁倾角、惯性导航系统(inertial navigation system，INS)与磁力计解算的航向角之差这三个特征值构建广义似然比检验量，对磁场进行判断获得稳定状态磁场信息，通过卡尔曼滤波对航向角误差进行补偿。将传感器置于足部进行实验，实验结果表明,两种算法可以实现优势互补，有效地抑制地磁信息的干扰，提高导航精度，算法结果相对于无航向角修正的零速修正算法、零角速度更新算法和单独使用磁力计进行融合的算法，定位误差分别降低了91.02%、82.93%和61.39%，室外和室内终点定位误差分别为4.545 4 m和0.543 6 m，占总路程的0.69%和0.17%。     

基于INS/GNSS紧耦合组合的逐步诱导式欺骗检测算法研究

传统惯性辅助GNSS欺骗信号检测方法对小于纯惯导误差漂移的逐步拉偏不敏感。针对这一问题,提出了一种基于INS/GNSS紧耦合组合的逐步诱导式欺骗信号检测方法。基于短时间内纯惯性导航结果和短时间惯导系统位置误差传播模型,利用惯导提供的位置和速度对伪距和伪距率时间序列进行预测估算,并结合伪距、伪距率实际测量结果,分别构造位置/伪距和速度/伪距率时间序列模型做参数拟合。基于真实信号得到的模型参数都是由惯导系统误差造成的,具有一致性;而基于逐步诱导式欺骗信号得到的模型参数还与欺骗策略的设定、载体飞行轨迹的观测以及干扰源的位置有关,具有不一致性。通过对比模型参数可判别欺骗信号的存在,对于真实信号,采用紧耦合组合导航修正INS输出;对于欺骗信号,继续惯性导航并采取相应措施。最后,根据逐步诱导式欺骗原理进行实验仿真,验证了算法的可行性和有效性。     

基于场景识别的惯性基类脑导航方法

针对无人机导航中惯性器件产生的漂移误差不断随时间累积进而影响导航精度的问题,以仿生类脑导航为研究背景,提出了一种新的导航方法。与传统导航策略不同的是,该方法从周期性校正累积位置误差的角度出发,采用设置位置细胞节点的思路,利用训练好的卷积神经网络模型在细胞节点处进行图像匹配,从而在位置细胞节点处实现惯导位置漂移误差校正。同时,建立节点之间的漂移误差模型,调整误差方程系数,以达到修正漂移误差的目的。最后,基于无人机飞行实验结果验证了该方法对自主导航的有效性和鲁棒性,该方法能够有效提高无人机导航的精度。     

双轴旋转式激光捷联惯导系统的转位方案研究

惯性导航系统的误差随时间累积,旋转调制技术可以有效地提高惯导系统的长航时精度,旋转调制方案是决定旋转式捷联惯导系统导航精度的一个重要因素.针对双轴旋转惯导系统,相较于16次序转位方案,提出了一种新的32次序双轴旋转调制方案.根据捷联惯导系统的误差方程,推导出旋转捷联惯导的误差方程,分析了误差补偿的机理,研究了惯性器件常值偏置误差、标度因数误差和安装角误差的传播特性.仿真结果表明,32次序双轴旋转调制方案相对于16次序转位方案有明显的优势,可以有效地降低姿态角误差和经纬度误差.     

Gravity modeling in aerospace applications

The science of inertial navigation has evolved to the point that the traditional gravity model is a principal error source in advanced, precise systems. Specifically, the unmodeled vertical deflections of the earth's gravitational field are a major contributor to CEP (circular error probable) divergence in precise terrestrial inertial navigation systems (INS). Over the years, several studies have been undertaken to the development of advanced techniques for accurate, real-time compensation of gravity disturbance vectors. More complex on-board gravity models which compute vertical deflection components will reduce the CEP divergence rate, but imperfect modeling due to on-board processing limitations will still cause residual vertical deflection errors. In order to eliminate or reduce gravity-induced errors in the INS requires measurement of gravity disturbance values and in-flight compensation to the inertial navigator. It is assumed in this paper that gravity disturbance values have been measured prior to the airborne mission and various techniques for compensation are to be considered. As part of a screening process in this study, several gravity compensation techniques (both deterministic and stochastic models) were investigated. The screening process involved identification of gravity models and algorithms, and developments of selection criteria for subsequent screening of the candidates.     

MEMS陀螺仪的高精度标定方法

为了提高微机电(MEMS)陀螺仪的测量精度，研究了一种同时标定陀螺非正交误差和加速度敏感漂移误差的标定方法。设计了16位置的转台标定方案，分别以地球自转角速率和重力加速度作为角速率和加速度激励源，利用两组角速率数据迭代求解非正交误差和加速度敏感漂移误差，并以陀螺仪对地球自转角速率的敏感误差作为校正效果的评估依据。试验结果表明，该方法能够有效校正MEMS陀螺仪的非正交误差和加速度敏感漂移误差，提高了陀螺仪的测量精度，且易于工程实现。