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     

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.     

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。     

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     

DQI-NP application to the OutriderTM Tactical UnmannedAerial Vehicle (TUAV)

A wide variety of applications can benefit from integrated Inertial Navigation System/Global Positioning System (INS/GPS) technology. However, in many situations, the end user has a preference for a specific GPS receiver. Additionally, in most cases, the user does not desire to expend the time and money necessary to perform a custom INS/GPS integration, but instead wants a low-cost off-the-shelf solution. To address these applications, Boeing has developed the Digital Quartz Inertial Measurement Unit (DQI IMU)-Navigation Processor (DQI-NP) product as an extension of its Miniature Integrated GPS/INS Tactical System (MIGITS^TM) family of integrated INS/GPS products. This paper describes the DQI-NP and its application to the Outrider^TM Tactical Unmanned Aerial Vehicle (TUAV). The DQI-NP, as currently integrated into the Outrider^TM TUAV, is coupled with a custom Trimble GPS receiver combined with major embedded firmware modifications by IntegriNautics. In conjunction with differential GPS and ground based pseudolites, the overall system is intended to provide autonomous landing capability to the Outrider TUAV. DQI-NP provides an available, low-cost, commercial-off-the-shelf/non-development item (COTS/NDI) solution to a variety of commercial and military applications, of which the Outrider ^TM TUAV is an excellent example     

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.     

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     

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     

Prototype personal navigation system

Honeywell Laboratories recently funded the development of a prototype personal navigation system based on MEMS technologies. The system components include a MEMS inertial measurement unit, a three-axis magnetometer, a barometric pressure sensor, and a SAASM GPS receiver. The system also uses Honeywell's human motion-based pedometry algorithm. The navigation process is based on a strap-down inertial navigator aided by feedback from a Kalman filter using typical measurements from the GPS, magnetometer and barometer when available. A key innovation is the addition of an independent measurement of distance traveled based on the use of a human motion algorithm. The navigation system combines the best features of dead reckoning and inertial navigation, resulting in positioning performance exceeding that achieved with either method alone. Subsequent to the Honeywell effort, DARPA funded an individual Personal Inertial Navigation System (iPINS) seedling program. Honeywell worked to improve the baseline personal navigation system with the objective of demonstrating the feasibility of reliably achieving navigation accuracy < 1 % of distance traveled in GPS-denied scenarios. In addition, an analysis was conducted to determine the benefit of incorporating terrain correlation into the personal navigation system. The results of this analysis indicate that overall navigation accuracy can be significantly improved through the application of terrain correlation. This presents an are presented. In addition, conclusions from the terrain correlation analysis conducted under the iPINS seedling program are included.     

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.     

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.     

GPS/SINS超紧组合导航的性能分析

GPS接收机在高动态环境下很容易失锁,特别是载体的高动态造成的应力对接收机载波跟踪环影响很大。为了解决高动态环境下的组合导航,分析了GPS接收机载波跟踪环的测量误差和跟踪门限,采用惯导速度辅助GPS接收机跟踪环路的超紧组合结构。超紧组合需要涉及到GPS接收机跟踪环内部编排及高动态环境下的实验数据,难度较大。针对超紧组合仿真专门开发了GPS实时软件接收机、高动态GPS中频信号仿真器和惯导模拟器并构建了一个完整的GPS/SINS超紧组合仿真系统。仿真结果表明,该超紧组合导航系统可以跟踪50g的加速度和10倍音速。     

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.     

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。     

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     

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%。     

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.     

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。     

Differential GPS/inertial navigation approach/landing flight testresults

In November 1990, a differential GPS/inertial flight test was conducted to acquire a system performance database and demonstrate automatic landing using an integrated differential GPS/INS with barometric and radar altimeters. Flight test results obtained from postflight data analysis are presented. These results include characteristics of DGPS/inertial error, using a laser tracker as a reference. In addition, data are provided on the magnitude of the differential correlations and vertical channel performance with and without radar altimeter augmentation. Flight test results show one sigma DGPS/inertial horizontal errors of 9 ft and one sigma DGPS inertial vertical errors of 15 ft. Without selective availability effects, the differential corrections are less than 10 ft and are dominated by receiver unique errors over the time period of an approach. Therefore, the one sigma performance of the autonomous GPS (8-ft horizontal and 20-ft vertical) is very similar to the DGPS/inertial performance. Postprocessed results also demonstrate significant improvements in vertical channel performance when GPS/inertial is aided with radar altimeter along with a low-resolution terrain map