高精度陆用惯导零速修正技术研究

零速修正技术是提高陆用惯导定位精度的一项有效手段。在周期性停车过程中,惯导系统除了将速度误差清零外,还能将由陀螺漂移、平台误差角等因素引起的位置误差进行补偿。本文采用离散线性卡尔曼滤波方法,通过连续三个停车时刻的速度误差构造观测向量,对惯导速度误差、加速度误差和陀螺漂移进行估计,进而补偿位置误差。车载试验表明,该零速修正方法位置精度高,且停车时间短,能够满足地面车快速响应的要求。     

基于对偶四元数的INS/GPS组合导航算法研究

在基于对偶四元数的捷联惯导解算方法的基础上,推导了以惯性系作为导航系的惯导误差方程,在此基础上设计了卡尔曼滤波组合导航算法。通过激光惯导跑车采集数据,进行了仿真分析,试验结果表明,该组合导航算法能有效的消除惯导累积的速度误差和位置误差,相比于目前广泛应用的INS/GPS组合导航算法,本文描述了INS/GPS组合导航的另一种实现方式,获得了相当的精度,具有一定的工程应用价值。     

基于联邦滤波结构的INS／GPS组合导航系统数据融合研究

为了研究平台式惯导INS（interial navigation system）和全球定位系统GPS（globe position system）组合导航联邦滤波器的实现,使用速度局部滤波器和位置局部滤波器,分别对INS／GPS组合导航系统的向东速度、向北速度,以及对经度和纬度进行卡尔曼滤波,然后将位置数据和速度数据输入主滤波进行数据融合。以无人机的向东匀速水平飞行为背景,运用联邦卡尔曼滤波器算法,使用matelab进行仿真分析。可以证明联邦滤波器算法简单,易于实现,并且可以提高导航系统精度．实际应用中此方法可行。     

Multipath-adaptive GPS/INS receiver

Multipath interference is one of the contributing sources of errors in precise global positioning system (GPS) position determination. This paper identifies key parameters of a multipath signal, focusing on estimating them accurately in order to mitigate multipath effects. Multiple model adaptive estimation (MMAE) techniques are applied to an inertial navigation system (INS)-coupled GPS receiver, based on a federated (distributed) Kalman filter design, to estimate the desired multipath parameters. The system configuration is one in which a GPS receiver and an INS are integrated together at the level of the in-phase and quadrature phase (I and Q) signals, rather than at the level of pseudo-range signals or navigation solutions. The system model of the MMAE is presented and the elemental Kalman filter design is examined. Different parameter search spaces are examined for accurate multipath parameter identification. The resulting GPS/INS receiver designs are validated through computer simulation of a user receiving signals from GPS satellites with multipath signal interference present The designed adaptive receiver provides pseudo-range estimates that are corrected for the effects of multipath interference, resulting in an integrated system that performs well with or without multipath interference present.     

A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer

The state estimation strategy using the smooth variable structure filter(SVSF) is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy.This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters(EKF), nonlinear H1 and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system(SINS) and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width.Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle(UAV) position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.     

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.     

Kalman filter mechanization for INS airstart

A strapdown mechanization and associated Kalman filter are developed to provide both ground align and airstart capabilities for inertial navigation systems (INSs) using Doppler velocity and position fixes, while not requiring an initial heading estimate. Position update during coarse mode is possible by defining sine and cosine of wander angle as filter states and modeling the position error in geographic frame while integrating velocity in the wander frame. INS Global Positioning System (GPS) differential position due to GPS antenna moment arm can aid heading convergence during hover turns in helicopter applications. Azimuth error state in the fine mode of the filter is defined as wander angle error to provide continuous estimation of navigational states, as well as inertial/aiding sensor errors, across the coarse-to-fine mode transition. Though motivated by a tactical helicopter application, the design can be applied to other vehicles. Advantages over conventional systems in addition to the airstart capability include robustness and versatility in handling many different operational conditions     

Accurate INS Transfer Alignment Using a Monitor Gyro and External Navigation Measurements

A particular alignment mechanization for a nonmaneuvering vehicle is described which uses a monitor gyro to estimate the slave inertial navigation system (INS) equivalent east gyro drift rate and thus improves azimuth alignment. A state-space model of the dynamic system with measurements is developed. Results of covariance simulations employing Kalman filter estimation are presented for two master INS position update scenarios, one involving frequent and very accurate updates and the other including infrequent and coarse updates. The ef fects of position updates and the monitor gyro on the quality of transfer alignment are demonstrated and analyzed.     

一种双轴旋转惯性导航系统的重要参数快速自标定方法

为了提高双轴旋转惯导重要参数标定的快速性和精度，提出一种快速自标定方法。通过设置不同的标定路径可以在10 min内完成陀螺和加速度计的零偏以及标度因数误差的标定。该方法利用基于姿态误差观测的卡尔曼滤波完成陀螺零偏的估计。通过六位置翻滚并以速度误差作为观测量进行卡尔曼滤波，完成加速度计的零偏及标度因数误差的标定。使天向陀螺绕方位轴旋转4周，使水平陀螺绕水平轴转动4周，通过计算旋转前后的姿态误差完成陀螺标度因数误差的估计。仿真和试验结果表明，该方法可以实现双轴旋转惯导重要参数10 min内完成自标定，且具有较高的精度。     

基于联邦滤波的容错组合导航系统仿真分析

设计INS／GPS组合导航系统时,考虑到观测量GPS位置和速度是正相关的,可通过降低单个滤波器的维度形成两个局部滤波器,主滤波器融合局部滤波器的状态估计,得到整个组合导航系统的误差状态估计值。同时,根据各局部滤波器的故障情况选择输出,仅利用未失效系统的局部滤波器得到可靠的最优误差状态估计值,使得容错性能大大提高。结果表明,由于采用了并行运算,增加了系统的余度,有效提高了导航系统的精度和可靠性,有较好的容错性和环境适应性,具有较高的应用价值。     

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.     

GPS/惯性组合方式讨论与导航精度分析

 本文讨论GPS/惯性组合两种方式的优缺点。并以GPS伪距和伪距率与惯导组合为例,按GPS测量误差的不同,以及使用差分机等情况,仿真计算了机载使用的组合导航性能,进行了详细的精度分析。结果表明,这种组合的导航精度比GPS和惯导各自的导航精度高。在采用差分GPS机与惯导组合后,位置误差将进一步减少,使组合导航具有开辟例如飞机进场着陆等新的使用领域的可能性。     

卡尔曼滤波在某型组合导航系统模拟器中的应用

为提高某型GPS／INS组合导航系统模拟器模拟数据的真实性和飞行软件包、GPS模拟器、组合导航系统模拟器三者交联的有效性,在该模拟器中设计了卡尔曼滤波器。文中在介绍模拟器工作原理的基础上,建立了GPS／INS位置与速度组合方式下的卡尔曼滤波器的状态方程和量测方程,用U-D分解法建立了卡尔曼滤波方程,给出了纯惯导及组合后系统的位置与速度误差仿真曲线,并对仿真结果进行了系统测试,最后与其它模拟器进行了组网导航训练测试。     

舰载捷联惯导系统的航行中快速在线标定

为实现舰载捷联惯导系统在航行中被快速标定的新需求，提出了一种捷联惯导系统在航行中快速在线标定的方法。首先，建立了简化的陀螺和加速度计输出误差方程，从而对Kalman滤波模型实现了降维。该模型以陀螺和加速度计零偏、标度因数误差等15个误差量为状态量，以速度误差和位置误差为量测量。设计了一种标定路径，该标定路径可由惯导系统中的双轴旋转机构实现。仿真结果表明，该方法能够在1800s内快速、准确地估计出15个误差量，具有工程实践价值。     

基于可观测性分析的旋转式惯导系统在线自标定方法的设计

本文建立了速度误差外观测量的静基座双轴旋转式惯导系统在线标定卡尔曼滤波模型,其状态向量包括地速误差、姿态失准角和惯性器件零偏、标度因数误差、安装误差,可估计旋转式惯导系统失准角与惯性器件误差参数。通过分段线性定常系统(PWCS)可观测性分析方法分析不同旋转方式下系统可观测性变化情况,得出双轴连续旋转的角运动方案可以改善卡尔曼滤波滤波的可观测性。根据基于奇异值分解的可观测度分析结果进行模型降阶,同时结合旋转式惯导系统的工程应用特性,得到12阶卡尔曼滤波参数模型。降阶系统阶数降低约55%,可以显著降低运算量,有效提高了导航计算机运算效率和实时性。仿真实验表明：降阶模型的估计精度不低于原模型,而且部分状态量的滤波收敛速度有提高。     

Enhancing filter robustness in cascaded GPS-INS integrations

Filter robustness is defined herein as the ability of the Global Positioning System/Inertial Navigation System (GPS-INS) Kalman filter to cope with adverse environments and input conditions, to successfully identify such conditions and to take evasive action. The formulation of two such techniques for a cascaded GPS-INS Kalman filter integration is discussed This is an integration in which the navigation solution from a GPS receiver is used as a measurement in the filter to estimate inertial errors and instrument biases. The first technique presented discusses the handling of GPS position biases. These are due to errors in the GPS satellite segment, and are known to be unobservable. They change levels when a satellite constellation change occurs, at which point they introduce undesirable filter response transients. A method of suppressing these transients is presented. The second technique presented deals with the proper identification of the filter measurement noise. Successful formulation of the noise statistics is a factor vital to the healthy estimation of the filter gains and operation. Furthermore, confidence in the formulation of these statistics can lead to the proper screening and rejection of bad data in the filter. A method of formulating the filter noise statistics dynamically based on inputs from the GPS and the INS is discussed     

在组合导航系统中Kalman滤波技术的应用

在导航系统中引入Kalman滤波技术,主要是为了减小导航定时的参数误差并提高系统的定位精度,以INS/GPS组合导航系统为背景,设计位置速度组合模式的卡尔曼滤波器,并对组合导航系统进行仿真研究,结果表明组合导航系统在导航精度和稳定性方面较单一的导航系统都有提高。     

Balloon gravimetry using GPS and INS

Measurement of the horizontal components of gravity at altitude using balloon-borne instrumentation consisting of a Global Positioning System (GPS) receiver and a strapdown inertial navigation system (INS) is discussed. GPS data are to be used primarily to determine the total inertial acceleration of the balloon, while the INS accelerometers sense all nongravitational accelerations. A covariance analysis based on the Kalman filter shows that conventional gravity estimation from GPS-aided INS data is possible only if external attitude updates are also available. An alternative technique is explored that attempts to estimate at least part of the gravitational spectrum without modeling the gravity disturbance as a state variable or relying on external attitude updates, while, at the same time, admitting uncorrected (long-wavelength) attitude errors. Simulations based on a model for typical balloon motion are used to discuss this possibility     

Spectral Analysis of Optimal and Suboptimal Gyro Monitoring Filters

Gyro monitoring filters are used to estimate and correct gyro drift rates of local-level inertial navigation systems. Conventional gyro monitoring filters are usually designed based on a simplified model of gyro drift rate. Furthermore, the effectiveness of these filters-and of many filters of the "Kalman" type-is often measured in terms of the root mean square (rms) criterion in contrast to the spectral content criterion typical of classical Wiener filtering theory. This paper has two objectives: to propose a gyro monitoring filter which is based on a more detailed model of gyro drift rate; and to propose a method of filter performance evaluation which uses as criterion a measure of the spectral content of the error process. The proposed gyro monitoring filter is shown to have improved spectral contents resulting in superior navigation performance for the gyro error models used in the calculations (comparable to commercial-grade aircraft gyros).