GPS/INS integration on the Standoff Land Attack Missile (SLAM)

The Standoff Land Attack Missile (SLAM) is a worldwide, all-weather, precision-strike weapon system deployed from carrier-based aircraft. In the primary mode of operation, target location and other mission data are generated from intelligence sources available on the aircraft carrier and loaded into the missile prior to aircraft takeoff. After missile launch, the SLAM inertial navigation system (INS) guides the missile along the planned trajectory. Updating the missile INS from the Global Positioning System (GPS) during flight provides precise midcourse navigation and enhances target acquisition by accurate, on-target pointing of the SLAM Maverick seeker. The GPS/INS avionics and software integration used for SLAM are described in detail, along with some of the design tradeoffs that led to the approach. The avionics configuration integrates the Harpoon midcourse guidance unit, which includes a strapdown inertial sensor package and digital processor, with a Rockwell-Collins single-channel, sequential GPS receiver processor unit (RPU), a derivative of the GPS phase-III user equipment. In addition to the GPS receiver elements the RPU contains the navigation processor, which executes the SLAM navigation, Kalman filter algorithms, and other guidance algorithms including seeker pointing. Flight-test results of the SLAM GPS-aided INS are also included     

Autonomous GPS/INS navigation experiment for space transfer vehicle

An experiment is described to validate the concept of developing an autonomous integrated spacecraft navigation system using onboard Global Positioning System (GPS) and inertial navigation system (INS) measurements. Previous work by the authors (1988, 1990) has demonstrated the feasibility of integrating GPS measurements with INS measurements to provide a total improvement in spacecraft navigation performance, i.e., improvement in position, velocity and attitude information. An important aspect of this research is the automatic real-time reconfiguration capability of the system, which is designed to respond to changes in a spacecraft mission under the control of an expert system     

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。     

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。     

平流层飞艇升降阶段BPNN惯性导航算法

软式平流层飞艇艇体在上升和下降时经常呈堆叠状态，GPS信号会被艇体间歇性遮挡，因而只能采用惯性导航。为保证在飞艇上升和下降过程中，INS/GPS组合导航系统在被艇体遮挡GPS时仍能够提供满足精度要求的导航信息，设计了一种改进的反向传播神经网络(Back Propagation Neural Network, BPNN）惯性导航算法。采用神经网络，根据惯性导航系统在1s内的速度均值和姿态变化量，预估其在1s末的位置误差和速度误差，并对惯性导航结果进行修正。仿真实验和跑车试验结果表明，在GPS失效的30s内，新算法使得位置误差低于15m，速度误差低于0.7m/s，误差相比纯惯性导航降低了85%。     

改进概率关联算法在组合导航系统中的应用研究

针对组合导航系统中子系统提供的量测信息相互之间关联性不足的问题,在组合导航系统中引入改进概率关联算法,通过改进的概率关联算法将这些导航信息按一定关系联系起来并进行融合计算。首先通过推导短时间概率关联算法求解惯性导航系统(INS)得到信息的可信概率,并引入掩模算子对全球定位系统(GPS)信息进行精度判断;然后,推导出长时间概率关联算法对组合导航系统信息进行融合;最后,建立修正算法并利用长时间概率关联算法得到的信息对惯性导航系统进行误差修正。仿真结果表明,这一改进的概率关联算法可以有效判断GPS信息精度并提高组合导航系统的导航精度。     

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.     

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 Aided Inertial Navigation

The Global Positioning System is an extremely accurate satellite-based navigation system which, after its completion in 1989, will provide users worldwide, 24 hour. all weather coverage. A joint research project among Boeing, Rockwell-Collins, and Northrop has been completed in which a GPS receiver was integrated with a low-cost strap-down inertial navigation system and a flight computer. A Kalman filter in the latter allows in-fight alignment and calibration of the INS. In addition, feedback from the INS to the GPS receiver improves the system's ability to reacquire satellite signals after outages. The resulting system combines the accuracy of GPS with the jamming immunity and autonomy of inertial navigation. System tests were conducted in which a Boeing owned T-33 jet aircraft was flown through known test pattern to align and calibrate the INS. Earlier tests, including tests against an airborne jammer, were conducted in a modified passenger bus.     

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。     

一种适用于跑步状态的惯性/零速/GPS室内外无缝组合导航定位方法

目前，行人导航定位技术已经深入社会的众多领域，受到诸多学者的广泛关注。针对行人跑步状态，研究了一种惯性/零速/GPS室内外无缝组合导航定位方法。首先提出了可靠的、适用于行人跑步零速检测的方法，有效提高了在行人跑步状态下的零速检测的准确性。针对GPS信号容易受到高楼、高架等环境的干扰及在室内容易完全丢失的特点，提出了基于BP神经网络的GPS可用信号筛选方法，提高了GPS信息的可靠性与精准性。在此基础上，研究了基于可变量测的Kalman滤波器，实现了惯性/零速/GPS信息的有效融合，显著提高了在行人跑步状态下的导航定位精度。试验结果表明，所提出的这种适用于跑步状态的惯性/零速/GPS室内外无缝组合导航定位方法的平均定位误差可减小到行人跑步总里程的1%以内。     

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.     

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。     

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     

车载捷联惯导与里程计组合导航技术研究

惯导的误差随着时间增长是积累的,可采用里程计辅助捷联惯导构成纯自主的车载组合导航系统.利用捷联惯导的速度和里程计测量的速度之差作为观测量,通过卡尔曼滤波技术校正惯导的导航参数,可以有效地抑制惯导误差的积累,提高导航参数的精度.本文推导了组合导航系统的模型,从理论上用特征值方法分析了系统的可观测度,进而设计轨迹进行了仿真...     

Low cost inertial/GPS integrated position and orientation systemfor marine applications

This paper describes the Low Cost Position and Orientation System (LCPOS), comprising the integration of a low cost “tactical grade” inertial measurement unit with GPS position, velocity and heading aiding data. LCPOS was designed to be a cost-effective alternative to a high performance costly ship INS (SINS). The demonstrated accuracy of the LCPOS integrated navigation solution is comparable to that of the SINS     

小型无人机的导航与制导仿真系统研究

研究了基于飞行器动力学模型的小型无人机的惯性组合导航与制导系统仿真技术,组合导航系统嵌入了飞行器的轨迹和姿态控制回路,建立了基于MATLAB/AEROSIM的组合导航计算机仿真系统,仿真系统包括惯性组合导航仿真模块、传感器仿真支持模块、飞行器6自由度动力学模型模块、飞行器轨迹生成、轨迹控制与姿态控制模块、导航仿真评估模块。基于该仿真系统,目前已开展惯性/卫星深组合、惯性/大气数据融合等理论算法和技术研究。     

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.