多元信息融合技术在导航定位中的研究与应用

随着导航技术的发展,现有导航系统种类愈加繁多、功能愈加强大。但各个导航系统有自己最适合的应用方向,也会存在着各种各样的优缺点,无法在所有的复杂的环境下都能很好地发挥功能,可以通过信息融合将两种或多种导航系统通过融合,达到提高导航精度和稳定性的目的。本文通过卡尔曼滤波器对惯性导航系统及卫星定位系统进行融合,并用MATLAB对惯性定位系统及组合导航系统进行仿真,对其定位结果及误差进行对比分析,推断通过信息融合对于导航定位精度及稳定性的影响。

基于多元信息融合的GPS导航定位跟踪研究

高新科技的日益发展中,GPS导航技术也在快速发展。当前导航系统的种类越来越多,方向也越来越广,但是在各类导航系统中总是无可避免地存在着一些需要完善的问题,如不能在复杂环境中精确导航等。在科学技术的支撑下,相关方面可以通过信息融合的方法,以两种或多种导航的有机结合实现导航的稳定性,提高导航的精准度。基于卡尔曼滤波器,借助MATLAB仿真,分析惯性导航系统与卫星定位系统融合对导航定位精准度及稳定度的促进作用。

DESIGN & REALIZATION ON GPS/INS INTEGRATED NAVIGATION SYSTEM

This paper deals with the research of the GPS/INS integrated navigation system applying Extended Kalman Filter, which involves integrated principles, scheme and technology of combining with real INS and GPS receiver data. Emphases are placed on the modeling of system errors and implementation of the integrated system. Both loose and tightly coupled GPS/INS integrated in schemes are analyzed. On the basis of our experience accumulated in the research of GPS/INS for many years, the GPS/INS integrated navigation developing system is developed. It can be put into efficient and economic use in the study and design of integrated navigation system. It plays an important role in the aeronautical and astronautical fields in China. This system is not only a computer aided design software but also a semi physical simulation system by obtaining real INS and GPS receiver data. So the key software unit of the developing system could be conveniently transferred into practical engineering software in actual hardware integrated system. The application of this system shows that the design ideas and integrated scheme of this development system are successful, and can achieve good navigation result.

Application of unscented Kalman filter to novel terrain passive integrated navigation system

To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.

SAR MOTION INFORMATION SENSOR BASED ON GNSS/SINS

Synthetic aperture radar (SAR) is theoretically based on uniform rectilinear motion. But in real situations, the flight cannot be kept in a uniform rectilinear motion due to many factors. Therefore, the motion compensation is needed to achieve the high-resolution image. This paper proposes an improved motion information sensor (MIS)-based on global navigation statellite system (GNSS) and strapdown inertial navigation system (SINS) for SAR motion compensation. MIS can provide the long-term absolute accuracy, and the short-term high relative accuracy during SAR imaging. Many issues related to MIS, such as system design, error models and navigation algorithms, are stressed. Experimental results show that MIS can provide accurate navigation information (position, velocity and attitude) to meet the requirements of SAR motion compensation. Especially, MIS is suitable for the case: the accuracy of airplane master inertial navigation system is too low or not configured.

Anomaly detection method based on kinematics model and nonholonomic constraint of vehicle

A method used to detect anomaly and estimate the state of vehicle in driving was proposed.The kinematics model of the vehicle was constructed and nonholonomic constraint conditions were added,which refer to that once the vehicle encounters the faults that could not be controlled,the constraint conditions are violated.Estimation equations of the velocity errors of the vehicle were given out to estimate the velocity errors of side and forward.So the stability of the whole vehicle could be judged by the velocity errors of the vehicle.Conclusions were validated through the vehicle experiment.This method is based on GPS/INS integrated navigation system,and can provide foundation for fault detections in unmanned autonomous vehicles.

APPLYINGSUCCESSIVEORTHOGONALIZATIONDECENTRALIZEDKALMANFILTERTOGPS／INSINTEGRATEDNAVIGATIONSYSTEM

The Successive Orthogonalization Decentralized Kalman Filter (SODKF ) is a new method which is used for large system state estimation. It can be applied not only to large system decentralization, but also to precision realization at approximately the same level of the global filter, thus, making possible the engineering operation as well as shortening the computing time. This paper discusses the principles and features of SODKF when used in GPS/INS integrated navigation system. The system will be firstly divided into three subsystems and then corrected in both open and closed loops. The system simulation results by two integrated patterns show that SODKF is efficient and realizable. While the three subsystems are simulated in series, the computing speed doubles that of the global system. In addition, its optimal estimating precision remains unchanged. It can be concluded from this paper that large integrated navigation systems with GPS, INS, Terrain Match, Loran C, Doppler Radar and Radio Altimeter can be made more efficient by this multi subsystem of navigation.

Application of Adaptive Divided Difference Filter on GPS/IMU Integrated Navigation System

The efficient and accurate approximate nonlinear filters have been widely used in the estimation of states and parameters of dynamical systems. In this paper, an adaptive divided difference filter is designed for precise estimation of states and parameters of micromechanical gyro navigation system. Based on the investigation of nonlinear divided difference filter the adaptive divided difference filter(ADDF) was designed, which takes account of the incorrect time-varying noise statistics of dynamical systems and compensation of the nonlinearity effects neglected by linearization. And its performance is superior to that of DDF and extended Kalman filter(EKF). Simulation results indicate that the advantages of the proposed nonlinear filters make them attractive alternatives to the extended Kalman filter.

Integrity analysis of COMPASS and other GNSS combined navigation

The integrity requirement should be satisfied when GNSS is used in aviation.There are now two ways for integrity monitoring,that is,receiver autonomous integrity monitoring(RAIM) and GNSS integrity channel(GIC) based on augmentation system.The rapid development of the Beidou satellite navigation system(COMPASS) will significantly improve the satellite geometry.Besides,the effects of first order ionosphere delay will be mitigated by the combination of GNSS multi-frequency signals.The availability of RAIM will be enhanced,which makes it possible to provide a worldwide seamless integrity service for aviation by using RAIM.The contribution of COMPASS to the availability of RAIM is analyzed by simulation;and the integrity requirement of which aviation approaching phrase can be satisfied when using COMPASS single system and its combination with other satellite navigation system is analyzed.Moreover,the influence of user range accuracy(URA) of COMPASS on integrity performance is discussed.