New low cost avionics with INS/GPS for a variety of vehicles

Development of a new vehicle avionics suite is described, including integration of a low-cost, tightly-coupled integrated Inertial Navigation System/Global Positioning System (INS/GPS) to support vehicle guidance, navigation, and control (GN&C). A wide variety of next-generation low-cost launch vehicles could potentially benefit from integrated INS/GPS technology for GN&C and/or range safety applications. Coleman Aerospace Company (CAC) has developed a new low-cost avionics suite, the generic Integrated Mission Guidance & Tracking System (IMGTS), an open architecture, modular system that supports the requirements for various guidance applications and range safety tracking. As part of this development, Boeing North American, Inc. is supplying its Modular Miniature Integrated GPS/INS Tactical System (M-MIGITS^TM) Military-Off-The-Shelf (MOTS) INS/GPS product to support CAC's IMGTS GN&C     

Decentralized Filtering and Redundancy Management for Multisensor Navigation

Failure detection and redundancy management is discussed for avionics applications of integrated navigation involving coordinated use of multiple simultaneous sensor subsystems such as GPS, JTIDS, TACAN, VOR/DME, ILS, an inertial navigation system (INS), and possibly even Doppler AHRS. A brief high level survey is provided to assess the status of those techniques and methodologies advertized as already available for handling the challenging real-time failure detection, redundancy management, and Kalman filtering aspects of these systems with differing availabilities, differing reliabilities, differing accuracies, and differing information content/sampling rates. Following the status review, a new failure detection/redundancy management approach is developed based on voter/monitoring at both the raw data and at the filtered-data level, as well as using additional inputs from hardware built-in-testing (BIT) and from specialized tests for subsequent failure isolation in the case of ambiguous indications. The technique developed involves use of Gaussian confidence regions to reasonably account for the inherent differences in accuracy between the various sensor subsystems. Online estimates of covariances from the Kalman filter are to be used for this purpose (when available). A technique is provided for quantitatively evaluating both the probability of detecting failed component subsystems and the probability of false alarm to be incurred, which is then to be traded off as the basis for rational selection of the thresholds used in the automated decision process. Moreover, the redundancy management procedure is demonstrated to be amenable to pilot or navigation operator prompting and override, if necessary.     

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.     

GNSS/INS深组合技术研究进展与展望

在GNSS/INS组合导航领域,深组合是最深层次的组合方式,随着航空、航天、军事等应用对导航系统性能要求的提升,深组合技术逐步成为国内外研究的重点。通过对GNSS/INS深组合的技术起源与结构发展过程的梳理,总结出当前对深组合概念理解的两种立场。按照接收机环路跟踪结构与信息处理方式不同,将深组合结构分为标量深组合、相干矢量深组合以及非相干矢量深组合3种类型,其中相干矢量深组合又可以分为级联式和集中式两种结构。综述了国内外深组合技术的研究现状并指出了当前研究中存在的问题,最后结合实际应用需求对深组合的发展进行了展望。     

Fuzzy corrections in a GPS/INS hybrid navigation system

A new concept regarding GPS/INS integration, based on artificial intelligence, i.e. adaptive neuro-fuzzy inference system (ANFIS) is presented. The GPS is used as reference during the time it is available. The data from GPS and inertial navigation system (INS) are used to build a structured knowledge base consisting of behavior of the INS in some special scenarios of vehicle motion. With the same data, the proposed fuzzy system is trained to obtain the corrected navigation data. In the absence of the GPS information, the system will perform its task only with the data from INS and with the fuzzy correction algorithm. This paper shows, using Matlab simulations, that as long as the GPS unavailability time is no longer than the previous training time and for the scenarios a priori defined, the accuracy of trained ANFIS, in absence of data from a reference navigation system, is better than the accuracy of stand-alone INS. The flexibility of model is also analyzed.     

Design and Development of a Real-Time DSP and FPGA-Based Integrated GPS-INS System for Compact and Low Power Applications

Emphasis of the present work is on an elegant real-time solution for GPS/INS integration. Micro-electro mechanical system (MEMS) based inertial sensors are light but not accurate enough for inertial navigation system (INS) applications. An integrated INS/GPS system provides better accuracy compared with either INS or GPS, used individually. This paper describes an improved design and fabrication of a loosely coupled INS-GPS integrated system. The systems currently available use commercial off-the-shelf (COTS) hardware and are, therefore, not optimized for compact, single supply, and low power requirements. In the proposed system, a digital signal processor (DSP) is used for inertial navigation solution and Kalman filter computations. A field programmable gate array (FPGA) is used for creating an efficient interface of the GPS with the DSP. Direct serial interface of the GPS involve tedious processing overhead on the navigation processor. Therefore, a universal asynchronous receiver transmitter (UART) and dual port random axis memory (DPRAM) are created on the FPGA itself. This also reduces the total chip count, resulting in a compact system. The system is designed to give real time processed navigation solutions with an update rate of 100 Hz. All the details of this work are presented.     

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     

智能方法在惯性系统误差参数辨识中的应用

总结了目前我国提高惯性系统导航精度的技术途径,阐述了国内外惯性系统误差参数辨识方法的研究现状,介绍了当前滤波算法、智能优化算法和人工神经网络方法在惯性系统导航领域的应用情况以及存在的不足。最后,分析了空间飞行器惯性系统误差参数辨识技术的未来研究方向,即智能优化算法和人工神经网络方法等智能方法将在惯性系统误差参数辨识中发挥越来越重要的作用,通过将误差系数标定问题转换为参数辨识问题,采用智能方法在庞大的解空间内实现对惯性系统误差参数的快速辨识。     

Italian low cost GNSS/INS system suitable for mobile mapping

The first studies for the mobile mapping and creation of a vehicle for this kind of research was carried out by Canadian Researchers in the 1980s. Since then, these vehicles have been widely employed in several applications (road cadastre maps, terrestrial photogrammetry, road sign recognition, etc.) for both commercial and research purposes throughout the world. Many GNSS/INS vehicles which can be equipped in different ways with one or more GPS, inertial sensors, and one or several cameras, have been realized. A characteristic shared by most of these devices concerns the high costs of the sensors, of the realization, and of the maintenance. For this reason, a GNSS/INS system, that is suitable for any vehicle, made up of low-cost devices (two GPS receivers, an INS, and a camera rigidly placed on a metallic bar), have been designed and built by our research group. Two tests run at different velocities have been carried out to evaluate the reliability of the system. After a presentation of the system, the differences that were witnessed during the application of these calibration methods are explained herein.     

地磁辅助的无缝INS/GPS组合导航研究

INS/GPS组合导航已经成为当前无人机导航系统的主要实现形式,由于GPS信号容易受到干扰,在恶劣的电磁环境下信号易丢失,从而导致GPS卫星信号失锁而无法使用。地磁导航作为一种无源导航方法,其难以受到外界干扰且具有较强的自主性,从而为克服GPS在干扰情况下无法对INS误差实现持续无缝修正的不足提供了很好的途径。针对INS/GPS组合导航中GPS卫星信号失锁的情况,设计提出了使用地磁匹配导航进行辅助实现无人机无缝导航的实现方案,设计了基于地磁特征的地磁匹配算法和地磁匹配辅助的INS/GPS组合无缝自主导航算法,并通过仿真验证了采用地磁匹配辅助导航方法,可以在GPS无效的情况下,实现对INS导航误差的持续无缝修正,从而提高导航系统性能。     

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。     

Analysis of upgraded GPS internal Kalman filter

GPS receivers with provisions for inertial navigation system (INS) aiding are designed with internal Kalman filters that model generic INSs and process the basic GPS pseudorange and deltarange (range-rate) data to produce an output of inertially-smoothed, “GPS-derived” position and velocity. These Kalman filters model only the basic nine INS errors (position, velocity, and tilt) and do not model any INS gyro or accelerometer errors. It was found that a significant performance improvement could be achieved under conditions of degraded GPS satellite availability by augmenting this type of filter with the six INS gyro and accelerometer bias errors. It is, therefore, recommended that serious consideration be given to incorporating these states into the design of the GPS internal Kalman filter     

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。     

基于地标信息的车载惯导姿态修正技术

惯导与速率计构成的组合系统是机动作战车辆较常使用的自主定位定向系统,其惯导姿态精度将直接影响组合定位精度。文章利用车辆运动途中的地标信息,讨论了车载惯导姿态误差估计与修正技术,通过仿真分析了地标位置与车辆零速信息在惯导姿态修正过程中的作用。结果表明：利用地标信息是进行车载惯导姿态修正较为实用的途径,在此基础上可以有效提高车载惯导／速率计组合定位系统的精度。     

Multi-sensor optimal data fusion for INS/GPS/SAR integrated navigation system

INS/GPS/SAR integrated navigation system represents the trend of next generation navigation systems with the high performance of independence, high precision and reliability. This paper presents a new multi-sensor data fusion methodology for INS/GPS/SAR integrated navigation systems. This methodology combines local decentralized fusion with global optimal fusion to enhance the accuracy and reliability of integrated navigation systems. A decentralized estimation fusion method is established for individual integrations of GPS and SAR into INS to obtain the local optimal state estimations in a parallel manner. A global optimal estimation fusion theory is studied to fuse the local optimal estimations for generating the global optimal state estimation of INS/GPS/SAR integrated navigation systems. The global data fusion features a method of variance upper finiteness and a method of variance upper bound to ensure that the global optimal state estimation can be achieved under a general condition. Experimental results demonstrate that INS/GPS/SAR integrated navigation systems achieved by using the proposed methodology have a better performance than INS/GPS integrated systems.     

A relative navigation system for formation flight

A relative navigation system based on both the Inertial Navigation System (INS) and the Global Positioning System (GPS) is developed to support situational awareness during formation flight. The architecture of the system requires an INS/GPS integration across two aircraft via a data link. A fault-tolerant federated filter is used to estimate the relative INS errors based on relative GPS measurements and a range measurement obtained from the data link. The filter is constructed based on a reduced-order model of the relative INS error process. A method for analyzing the filter performance is presented. A case involving two helicopters in formation flight is studied under three different night trajectories to account for the effect of vehicle motion on the INS state transition matrix. The results of the covariance analysis are compared with actual night results over an instrumented test range.     

GPS／INS组合导航系统松、紧耦合性能比较

对GPS/INS组合导航系统的紧耦合算法进行了理论推导。详细分析了当INS元器件性能变差时,GPS/INS组合导航系统在不同耦合模式下的定位精度变化规律,得出采用紧耦合模式的组合导航系统比一般的松耦合方式能获得更好的定位精度;并通过仿真验证了该结论的正确。     

基于RTS滤波的GPS+BDS非差非组合PPP/INS紧组合模型

GPS高精度定位技术在动态复杂环境中,其定位精度、可靠性和连续性因卫星信号频繁失锁而变差。为此,提出了采用基于RTS滤波(Rauch-Tung-Striebel Filter)的GPS+BDS非差非组合PPP(Precise Point Positioning)与INS(Inertial Navigation System)紧组合模型的策略来克服GPS在动态定位中的弱点。其中,采用GPS+BDS双系统观测数据,可提高PPP解算中的可用卫星数,改善星站间定位几何强度和提高PPP收敛速度;采用PPP/INS紧组合,利用INS的自主定位特性和短期高精度特性,可有效改善复杂环境下的定位精度和连续性;采用RTS滤波,可进一步提高PPP/INS紧组合性能。首先推导了GPS+BDS非差非组合函数模型、PPP/INS紧组合函数模型和RTS滤波函数模型,然后利用一组车载动态数据,对动态GPS PPP、GPS+BDS PPP、GPS/INS紧组合、GPS+BDS PPP/INS紧组合和基于RTS的GPS+BDS PPP/IMU紧组合的定位、测速和定姿性能进行分析。实验结果表明,该方案可有效提高定位(58%～72%)、测速(74%～82%)和定姿(4%～23%)精度,特别是对卫星失锁期间的定位性能改善尤为明显。     

高超声速飞行器组合导航鲁棒滤波算法

高超声速飞行器由于其飞行环境的影响,使得GPS和星敏感器的量测噪声表现出非高斯特性。针对常规基于Kalman滤波的组合导航在非高斯噪声下性能下降的问题,提出了基于鲁棒滤波的高超声速飞行器组合导航算法。方法在惯性／GPS／＆异步量测建模的基础上,通过随机去耦将量测更新转化为线性回归问题,并基于M估计获得状态量最优估计。仿真结果表明,方法对非高斯噪声具有更好的鲁棒性,有效提高了高超声速飞行器组合导航系统的性能。     

基于可观测度分析和增量因子图的多源融合导航方法

复杂动态场景下高精度导航定位是自主无人系统决策规划与控制执行的基础。在城市峡谷、隧道、地下或室内停车场等场景下，卫星信号弱甚至丢失，严重影响了惯性/卫星/视觉等多源融合导航的精度和可靠性。为主动适应动态场景变化，迫切需要设计一种适用于跨场景的多源融合导航系统。基于动态时变系统的可观测度分析，在线度量惯性/卫星、惯性/视觉等组合导航因子的可信程度，进而采用因子图融合导航框架，根据各组合导航因子的可信程度，主动优化因子构建和增量平滑过程，以实现多传感器自适应融合导航与可靠定位。实验仿真表明：基于可观测度分析方法，能够在线优化因子图计算过程，提升了多源融合导航系统的环境适应性和跨场景能力，保证了复杂动态场景下自主无人系统跨场景导航模式切换和连续可靠的导航定位。