基于联邦滤波的SINS/GNSS/OD/高度计多源组合导航算法研究

针对城市情况下车载导航时单一导航源易受干扰的问题,提出了一种基于自适应联邦Kalman滤波的多源组合导航算法.该模型具有两级结构,由子滤波器进行各信息源局部估计后,通过主滤波器进行最优融合估计.融合具有不同工作特点的导航传感器的输出信息组成多源信息组合导航系统,从而提高了导航系统的精度和鲁棒性,且通过故障诊断算法实时检测并隔离故障信息源.给出了联邦滤波算法设计,并进行了实际车载实验.实验结果表明,该算法能够提高导航系统的稳定性及精度.     

基于惯性/偏振光/光流的六足步行机器人自主导航方法研究

针对惯性/卫星组合导航系统易受干扰或自主性不足等问题，引入偏振光传感器和光流传感器，分别建立航向角和速度量测方程，以辅助惯性导航系统，提出了一种基于惯性/偏振光/光流的自主导航方法。同时，为实现惯性传感器、偏振光传感器和光流传感器等多传感器的融合，设计了无迹Kalman滤波器。为验证该方法的有效性，以六足步行机器人为对象开展仿真和实验验证。结果表明，在没有卫星信号源的情况下，仅依靠机器人自身感知，可实现较高精度的机器人位姿估计，实现了不依赖于卫星导航信号的自主导航，提升了导航系统的自主性。     

基于因子图的无人机全源导航关键技术研究

针对DARPA提出的全源导航需要快速集成并重新配置任意导航传感器的要求,结合民用无人机的任务需求,通过概率图模型相关原理,对基于因子图的无人机全源导航关键技术开展研究工作.采用因子图对系统状态更新过程进行表示,实现系统状态的递推与更新,完成传感器信息的数据综合.仿真结果表明,该方法能在传感器可用性时变的情况下,将不同传感器的数据进行有效融合,确保系统导航定位精度,使载体满足不断变化的任务需求与环境变化的要求.     

A Fault-Tolerant Multisensor Navigation System Design

The problem of soft-failure tolerant estimation in navigationsystems composed of multiple inertial measurement clusters and oneor more reference sensors is addressed. A new approach ispresented that achieves containment of failed sensor data, andisolates the historic good data provided by the unfailed sensors.Multiple (local) estimates are computed where the estimates areconditioned on different subsets of the sensors. A statistical overlaptest is used to determine the validity of the local estimates, and afailed sensor can be identified from analysis of the invalid localestimates. After the time of detection the most accurate estimatebased on all but the failed sensor is identified. The results areapplied to a dual-inertial/Doppler radar navigation system andsimulation results are presented.     

面向智能驾驶的高精度多源融合定位综述

随着信息时代各行各业效率的提升，传统的人工驾驶交通系统已逐渐无法满足人们对高效率、低风险交通服务的需求，而智能驾驶技术的出现为这一领域带来了机遇。如今，以自动驾驶为代表的智能驾驶已经成为一种实用的深度交叉技术，其核心模块包括高精度定位、场景感知、决策规划与控制等。定位模块作为智能驾驶系统中最基本、最核心的功能模块，需具备高精度、高可用、低时延的性能特点。当前，结合高精度卫星导航、惯性导航以及环境感知的多源融合技术已成为实现泛在智能驾驶所公认的核心手段，通过充分利用车载传感器的量测信息可以实现精确、可靠的定位服务。从导航定位中常用的传感器技术出发，对当前智能驾驶领域涉及的高精度定位技术进行了全面的回顾，给出了主流的基于滤波和因子图优化的多源融合框架，并对代表性算法进行了整理。最后，总结了现阶段智能驾驶中高精度定位技术的发展现状，并对未来的发展趋势进行了展望。     

基于零速修正的行人导航关键技术及研究进展

在室内等卫星导航受限场景下，可采用MEMS惯性传感器对行人进行自主导航定位。采用MEMS惯性传感器的行人自主导航技术主要包括基于步长估计的行人航位推算和基于零速修正的行人导航算法两种方式。首先介绍了两种方式的基本原理，然后重点分析了零速修正的行人导航算法，阐述了相关的关键技术和研究进展。最后，对行人自主导航未来的发展趋势进行了展望，并指出了需要重点关注的技术难点。     

An intelligentized and fast calibration method of SINS on moving base for planed missiles

In order to minish the error of inertial sensors, the technology of neural networks is attempted to on-line calibration of a slave inertial navigation system mounted on planed missiles. Based on the time-varied specialty of slave inertial navigation system on a moving base, an input–output sample structure method is proposed, and to automatically calibrate and revise the error of inertial sensors of inertial navigation system. When a missile is appended under the wing and in free-flight, in order to solve the inconsistent problem of measurement's character of the inertial sensors, the error angles between the master inertial navigation system and the slave inertial navigation system are estimated in advance, then, the input samples of a neural network can correctly simulate the free-flight state. Furthermore, in order to make a learning algorithm of neural networks can satisfy real-time calibrating on a moving base, the traditional Newton algorithm is improved by using first differential coefficient to replace the approximate matrix of second differential coefficients. As a result, the training speed and precision of neural network are enhanced. The simulation results indicate that the method and algorithm are feasible.     

某型GPS/INS组合导航评估系统构建

介绍了某型GPS/INS组合导航评估系统的原理、结构形式,构建方法。经过仿真试验验证,此系统可以完成某型飞控系统导航算法的动态验证,同时还可以独立提供GPS模拟数据、对飞控系统进行测试、开展跑车试验等功能,具有简单实用、通用性强等特点。     

Algorithmic support of a small-sized navigation system on the basis of the magnetoinertial course transmitter

The algorithmic support of a small-sized navigation system on the basis of the magnetoinertial course transmitter is considered; the support makes it possible to significantly reduce accumulating errors that are due to incomplete data on wind parameters and decrease requirements for random errors of initial data sensors.     

Automotive-grade MEMS sensors used for general aviation

The attitude heading reference system (AHRS) provides data for primary flight instruments, head-up displays, autopilots, and moving map navigation systems. Advances in solid-state MEMS rate sensors, coupled with Kalman filter algorithms designed to mitigate high drift rates, provide the basis for low-cost, high-performance AHRS for general aviation. This paper describes the performance of a low cost, miniaturized AHRS using automotive-grade MEMS sensors. The performance of the system is detailed. The implications for certification of this class of system and fault tolerance are discussed.     

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.     

Using natural features for vision based navigation of an indoor-VTOL MAV

The use of natural features for vision based navigation of an indoor Vertical-Take-Off-and-Landing (VTOL) Micro Aerial Vehicle (MAV) named Air-Quad is presented. Air-Quad is a small four-rotor helicopter developed at the ITE.Such a helicopter needs reliable attitude information. The measurements of the used MEMS gyroscopes and accelerometers are corrupted by strong noise. To be useful, the MEMS sensors have to be part of an integrated navigation system with aiding through complementary sensors like GPS or the computer vision module presented here.In the computer vision module, feature points are detected and tracked through the image sequence. The relative rotation and translation of the camera are estimated using the two-dimensional motion of the feature points.The three-dimensional points in the scene are modeled with the image coordinates of their first sighting and their inverse depths. Only these inverse depths are estimated for the feature points. An efficient sparse bundle adjustment algorithm is used to improve the estimation of the scene structure and the navigation solution.It is shown that the use of the computer vision module greatly improves the navigation solution compared to a solution based only on MEMS sensors.     

旋转式SINS的自标校技术研究

惯性器件常值及慢变误差是影响捷联惯导系统精度的主要因素之一,所以在捷联惯导系统出厂前需要对常值及慢变误差参数进行标定。但这些误差参数会随时间发生变化,对于高精度捷联惯导系统,每次启动后需要对惯性器件的误差参数进行重新标校。针对光纤惯导系统,建立了IMU误差模型,并根据提出的旋转式捷联惯导系统自标校转位方案原则设计出了一种8位置自标校方案,对惯性器件标定参数进行激励和辨识,并建立了Kalman滤波状态方程及量测方程,对惯导系统误差参数进行在线标定。实验结果表明,该方案对其惯性器件误差参数能进行准确估计,具有一定的参考价值。     

基于虚拟地标的行人惯性SLAM算法

针对当前仅使用低成本惯性传感器的行人导航系统航向角存在较大累积误差的问题,研究了一种只使用惯性传感器信息的虚拟地标构建方法,并通过匹配虚拟地标点对位置和航向进行误差补偿.在此基础上,结合同时定位与建图(SLAM)方法的思想,实现了仅基于惯性传感器的SLAM,提高了行人导航的精度和可靠导航时间.试验结果表明,该方法能够有效消除纯惯性行人定位系统的累积误差,在2027m2的单层建筑物中空间位置误差小于10m.     

信息融合技术在组合导航系统中的应用

提出了无线电近程导航系统(RSRN S)与IN S/GPS的综合过程,探讨了RSRN S和IN S/GPS综合应用过程中的信息融合技术问题。采用GPS伪距、伪距率以及无线电近程导航定位与IN S组合,建立了卡尔曼滤波方程,设计了飞行轨迹,对相关的信息融合技术进行了初步讨论,并给出了惯性元件精度较低时组合系统的仿真输出。这种组合导航方案可以充分利用各种导航传感器的信息,有效提高导航系统的精度。     

不依赖于卫星的无人系统视觉/激光雷达感知与自主导航技术

智能化的无人系统在现代社会中起着重要作用,而对环境信息的准确感知以及自身位置的精准估计是无人系统智能、高效执行任务的核心基础,视觉与激光雷达传感器是无人系统常用的感知与导航传感器.近年来,随着应用场景的拓展,无卫星信号、无光等恶劣环境对无人系统的感知与自主导航技术提出了新的挑战.针对上述环境,对无人系统中视觉/激光雷达感知技术与自主导航技术及其进展进行了分析和总结.从感知和导航定位两个应用层面出发,深入讨论和分析了基于视觉和激光雷达手段的深度估计、目标检测、自主导航、地图构建等技术的机理差异、实现方法及特性.分析了目前国内外研究成果及进展,总结对比了目前的技术特点与局限性,并展望了未来无人系统感知与导航的关键技术挑战与发展趋势.     

A system for obstacle detection during rotorcraft low altitudeflight

An airborne vehicle such as a rotorcraft must avoid obstacles like antennas, towers, poles, fences, tree branches, and wires strung across the flight path. Automatic detection of the obstacles and generation of appropriate guidance and control actions for the vehicle to avoid these obstacles would facilitate autonomous navigation. The requirements of an obstacle detection system for rotorcraft in low-altitude Nap-of-the-Earth (NOE) flight based on various rotorcraft motion constraints is analyzed here in detail. It is argued that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors to be effective. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo. The optical flow-based motion analysis that is combined with on-board inertial navigation system (INS) to compute ranges to visible scene points is described. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis     

Application of nonlinear filtering to navigation system designusing passive sensors

The problem of navigation system design for autonomous aircraft landing is addressed. New nonlinear filter structures are introduced to estimate the position and velocity of an aircraft with respect to a possibly moving landing site, such as a naval vessel, based on measurements provided by airborne vision and inertial sensors. By exploring the geometry of the navigation problem, the navigation filter dynamics are cast in the framework of linear parametrically varying systems (LPVs). Using this set-up, filter performance and stability are studied in an H_∞ setting by resorting to the theory of linear matrix inequalities (LMIs). The design of nonlinear, regionally stable filters to meet adequate H_∞ performance measures is thus converted into that of determining the feasibility of a related set of LMIs and finding a solution to them, if it exists. This is done by using-widely available numerical tools that borrow from convex optimization techniques. The mathematical framework that is required for integrated vision/inertial navigation system design is developed and a design example for an air vehicle landing on an aircraft carrier is detailed     

启发式GPS导航引擎的设计与实现

随着GIS系统和导航系统的发展,建立一个高效的路径搜索引擎成为众多相关系统开发者的当务之急.从经典Dijkstra算法出发,分析了城市道路网的特定网络结构特点,将决策机制引入到路径搜索中来,设计了一个启发式GPS导航引擎.最后列举了该导航引擎进行了的对比测试数据,结果表明所提出的启发是智能导航引擎在嵌入式GPS导航系统中有较好应用前景.     

Reducing geometric dilution of precision using ridge regression

A signal processing technique is proposed for improving position-fix navigation system accuracy performance when the geometry of the navigation landmarks (e.g. sensors) are nearly collinear. In the navigation literature, the accuracy degradation associated with a nearly collinear measure geometry is termed the geometric dilution of precision (GDOP). Its presence causes not only the variance of the position estimates to be highly inflated but also any bias terms which may be present in the model. Since a nearly collinear predictor matrix is mathematically equivalent to GDOP, it is proposed to use the ridge regression technique in a navigation signal processor. A position-fix algorithm based on ridge regression reduces the bias and variance inflation caused by GDOP and the overall mean-squared position error as well. Ridge regression contains the GDOP-sensitive least-mean-square (LMS) estimator as a special case. Even with a matched model, GDOP can inflate the mean-square error (MSE) of the ordinary least-squares estimator, whereas the ridge regression technique chooses a suitable biased estimator that will reduce the MSE, which is the main goal. The ridge concept is extended to include GDOP-amplified bias errors. A simple range/range navigation system is analyzed to illustrate the underlying principles of ridge regression