Neural Network-based GPS/INS Integrated System for Spacecraft Attitude Determination

Global Positioning System （GPS） /Inertial Navigation System （INS） integrated system is continuously gaining research interests in many positioning and navigation fields. Kalman filtering-based integrated algorithm has some drawbacks on stability, computation load, robustness, and system observability performances. Based on neural network technology, a new GPS/INS integration filtering algorithm is studied for an integration scheme of the attitude determination GPS/INS integrated navigation system. Through some theoretic analysis, this algorithm not only has good estimation performance, but also has better robustness to the system model and noise than the traditional Kalman algorithm. To assess the performance of the proposed integrated model more deeply, some simulation is done to compare with the traditional Kalman filter model. The results indicate that the proposed model provides a significant improvement in some performance, such as accuracy, stability, robustness, and so on.

基于VB-EKF的GPS/INS松组合导航定位算法

针对应用在无人机(Unmanned Aerial Vehicle,UAV)中的全球定位系统/惯性导航系统(GPS/INS)松组合导航非线性系统受到外界噪声干扰导致量测噪声在滤波时不断变化,从而造成滤波精度下降等问题,提出一种变分贝叶斯扩展卡尔曼滤波(VB-EKF)算法。该算法利用EKF(Extended Kalman Filter)将非线性系统中的状态函数和量测函数展开为线性方程,并将两个不同的导航系统数据进行融合,避免了单系统导航定位发散的问题。考虑到组合系统中量测噪声的时变特性,引入变分贝叶斯算法进行改进,有效解决了系统滤波精度下降问题。仿真结果表明,VB-EKF较EKF算法可有效提高滤波稳定性,进而提高系统导航定位精度。

GPS与INS的组合定位技术研究

组合定位技术是一种新的导航技术,能够使多个导航系统实现信息的互补性和协同性。其中,GPS和惯导结合是一种较为理想的组合定位系统,而深度融合模式是目前GPS和惯导(INS)联合使用的最佳方式,因此,对深度融合模式的研究显得尤为重要。

GPS/INS组合导航终端的全向位置驱离算法研究

随着微惯性导航系统技术的发展,越来越多的无人平台装备了GPS/INS组合导航终端并具备了一定的抗欺骗能力,但现有的卫星导航欺骗方法难以满足对欺骗目标任意方向的位置驱离需求。为解决这一问题,提出了一种GPS/INS组合导航终端的全向位置驱离算法,可用于对非法无人平台管控的场景。该方法以卫星欺骗信号的设计为突破口,首先分析了卫星导航欺骗对组合导航位置和状态参数的影响,接着将卫星导航欺骗对组合导航状态参数的影响量作为求取欺骗信号的约束条件,进而确定位置驱离所需卫星欺骗信号的欺骗量,接着利用确定的欺骗量建立全向位置驱离模型,最终利用建立的模型生成对应的欺骗信号实现对组合导航终端的全向位置驱离任务。试验验证了本文设计的算法能够实现对组合导航终端360°任意方向的位置驱离。

APPLICATION OF INTERVAL KALMAN FILTER TO AN INTEGRATED GPS/INS SYSTEM

An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applicable only to linear interval systems, the extended interval Kalman filter (EIKF) algorithm for non linear integrated systems is developed. A high dynamic aircraft trajectory is designed to test the algorithm developed. The results of computer simulation indicate that the EIKF algorithm is consistent with the traditional SKF scheme, and is also effective for uncertain non linear integrated system.

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.

Integrated Data Processing Method for GPS and INS Field Test over Rocky Mountain

The method of integrated data processing for GPS and INS(inertial navigation system) field test over the Rocky Mountains using the adaptive Kalman filtering technique is presented. On the basis of the known GPS outputs and the offset of GPS and INS, state equations and observations are designed to perform the calculation and improve the navigation accuracy. An example shows that with the method the reliable navigation parameters have been obtained.

A FAST KALMAN FILTER FOR INTEGRATED GPS/INS BASED ON U-D FACTORIZATION

A fast U D factorization based Kalman filter for the 21 state integrated global positioning system and inertial navigation system (GPS/INS) is developed from the point of engineering implementation. The conventional Kalman filter is widely used for integration of GPS/INS, however, due to the model and numerical computation errors, the Kalman filter may diverge in engineering implementation. In order to solve this problem, an extended Kalman filter based on the U D factorization is proposed. Moreover, the high order integrated system suffers from the problem of long computation time, leading to difficulties in real time applications. An algorithmic approach is developed to improve the computational speed. A typical aircraft trajectory is simulated to compare the improvement in the computational speed and the navigation accuracy using the conventional Kalman filter and the fast Kalman filter based on the U D factorization. The results indicate that the methods proposed in this paper are very effective in overcoming these problems for the high dynamic integrated GPS/INS system.

Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation

To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.

Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.

Hybrid Kalman and unscented Kalman filters for INS/GPS integrated system considering constant lever arm effect

In inertial navigation system(INS) and global positioning system(GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit(IMU) of the INS, so the lever arm effect exists, which makes the observation equation highly nonlinear. The INS/GPS integration with constant lever arm effect is studied. The position relation of IMU and GPS's antenna is represented in the earth centered earth fixed frame, while the velocity relation of these two systems is represented in local horizontal frame. Due to the small integration time interval of INS, i.e. 0.1 s in this work, the nonlinearity in the INS error equation is trivial, so the linear INS error model is constructed and addressed by Kalman filter's prediction step. On the other hand, the high nonlinearity in the observation equation due to lever arm effect is addressed by unscented Kalman filter's update step to attain higher accuracy and better applicability. Simulation is designed and the performance of the hybrid filter is validated.

INTEGRATED GPS/INS OF PSEUDO-RANGE DELTA-RANGE SEM I-PHYSICAL SIMULATION SRESEARCH

In orderto furtherstudy theperform ance oftightly integrated navigation system ofGPS/ INS, a sem i-physicalsim ulation oftightly coupled system has been done based on the data gathered from the experim entof integrated system ofGPSand INS. The closed-loop Kalm an Filter and U-D discom pose algorithm have been used. The sim ulation results associated to four integrated m odels of pseudo-range, delta-range, pseudo-range and delta-range alternation, and pseudo-range and delta- range synthesis have been provided, and the actualeffects of variously integrated m odels have been analyzed. The results show that the pseudo-range and delta-range synthesis coupled m odelis the m osteffective to im provethe coupled system perform anceand the individualdelta-rangecoupled m od- elhad betterbe avoided in application.

Low-Cost GPSIINS Integrated Land-Vehicular Navigation System for Harsh Environments Using Hybrid Mamdani AFISIKF Model

Nowadays, GPS （global positioning system） receivers are aided by INS （inertial navigation systems） to achieve more precision and stability in land-vehicular navigation. KF （Kalman filter） is a conventional method which is used for the navigation system to estimate the navigational parameters, when INS measurements are fused with GPS data. However, new generation of INS, which relies on MEMS （micro-electro-mechanical systems） based low-cost IMUs （inertial measurement units） for the land navigation systems, decreases the accuracy and the robustness of navigation system due to their inherent errors. This paper provides a new method for fusing the low-cost IMU and GPS measurements. The proposed method is based on KF aided by AF1S （adaptive fuzzy inference systems） as a promising solution to overcome the mentioned problems. The results of this study show the efficiency of the proposed method to reduce the navigation system errors in comparison with KF alone.

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.

Position and attitude determination by integrated GPS/SINS/TS for feed support system of FAST

This paper presents a new measurement system based on integration method that can provide all-weather dependability and higher precision for the measurement of FAST's feed support system.The measurement system consists of three types of measuring equipments,and a processing software with the core data fusion algorithm.The Strapdown Inertial Navigation System(SINS)can autonomously measure the position,speed and attitude of the carrier.Its own shortcoming is that the measurement data diverge rapidly over time.SINS must combine the Global Positioning System(GPS)and the Total Station(TS)to obtain high-precision measurement data.The Kalman filtering algorithm is adopted for the integration measurement system,which is an optimal algorithm to estimate the measurement errors.A series of tests are carried out to evaluate the performance.For the feed cabin,the maximum RMS of the position is 14.56 mm,the maximum RMS of the attitude is 0.095,these values are less than 15 mm and 0.1°as the precision for measuring the feed cabin.For the Stewart manipulator,the maximum RMS of the position is 2.99 mm,the maximum RMS of the attitude is 0.093°,these values are less than 3 mm and 0.1°as the precision for measuring the Stewart manipulator.As a result,the new measurement meets the requirement of measurement precision for FAST's feed support system.

GPS与INS组合的便携式轨道类游乐设施轨迹研究

为检测轨道类游乐设施运行速度和轨迹,利用安卓智能手机内置的惯性导航系统(INS)、卫星导航系统(GPS)进行轨道类游乐设施运行加速度、角速度等数据进行采集分析;采用捷联惯性导航系统,建立轨道类游乐设施轨迹姿态解算模型,以卡尔曼滤波器为基础,在Matlab软件中构建松耦合模式的组合导航系统,对INS输出的速度、位置数据进行校正;在试验场地对游乐设施数据进行采集并进行处理,基于UG NX二次开发实现轨道游乐设施姿态信息显示、轨迹拟合。实验结果验证了使用安卓智能手机内置的INS\GPS组合导航系统采集轨道类游乐设施数据、运行轨迹的可行性,弥补了轨道类游乐设施姿态和轨迹测量相关研究的不足。

基于GPS/INS的无人机物流配送路径控制研究

随着科技的发展,通过利用无线电遥控设备和自备的程序控制装置操纵的无人驾驶的低空飞行器运载包裹,自动送达目的地,其优点主要在于解决偏远地区的配送问题,提高配送效率,同时减少人力成本。本文以GPS/INS技术为基础,通过深入分析无人机的运动轨迹,指出无人机物流配送路径的识别与控制方法,有利于提高无人机的速度和加速度,增加识别精度。

INS-GNSS Integrated Navigation Algorithm Based on TransGAN

With the rapid development of autopilot technology,a variety of engi-neering applications require higher and higher requirements for navigation and positioning accuracy,as well as the error range should reach centimeter level.Single navigation systems such as the inertial navigation system(INS)and the global navigation satellite system(GNSS)cannot meet the navigation require-ments in many cases of high mobility and complex environments.For the purpose of improving the accuracy of INS-GNSS integrated navigation system,an INS-GNSS integrated navigation algorithm based on TransGAN is proposed.First of all,the GNSS data in the actual test process is applied to establish the data set.Secondly,the generator and discriminator are constructed.Borrowing the model structure of generator transformer,the generator is constructed by multi-layer transformer encoder,which can obtain a wider data perception ability.The generator and discriminator are trained and optimized by the production countermeasure network,so as to realize the speed and position error compensa-tion of INS.Consequently,when GNSS works normally,TransGAN is trained into a high-precision prediction model using INS-GNSS data.The trained Trans-GAN model is emoloyed to compensate the speed and position errors for INS.Through the test analysis offlight test data,the test results are compared with the performance of traditional multi-layer perceptron(MLP)and fuzzy wavelet neural network(WNN),demonstrating that TransGAN can effectively correct the speed and position information when GNSS is interrupted,with the high accuracy.

因子图框架下里程计辅助GNSS/INS组合导航算法

在复杂观测环境下,GNSS/INS组合导航系统的GNSS信号易受干扰从而导致INS独立导航精度迅速下降。针对上述问题,本文基于因子图的里程计辅助GNSS/INS组合导航算法,利用里程计观测信息结合非完整性约束构建航向速度约束方程,同时采用能多次线性化计算和多次迭代的因子图优化方法进行参数估计。实际车载试验解算结果表明,在GNSS信号良好时,基于因子图方法比滤波方法具有更快的收敛时间,收敛速度提高了近10倍;在GNSS信号发生中断时,添加里程计辅助后组合导航系统在东向和北向分别提升了83%和89%。与传统的滤波融合手段相比,本文采用因子图优化后在东向和北向的定位精度分别有63%、70%的改善。

Modified Observation Model in Tightly-Coupled INS/GPS Integration

The conventional Kalman filter（CKF）is widely used in tightly-coupled INS/GPS integrated navigation systems.The linearization accuracy of the CKF observation model is one of the decisive factors of the estimation accuracy and therefore navigation accuracy.Additionally,the conventional observation model（COM）used by the filter may be divergent,which would result into some terrible accuracies of INS/GPS integration navigation in some cases.To improve the navigation accuracy,the linearization accuracy of the COM still needs further improvement.To deal with this issue,the observation model is modified with the linearization of the range and range rate equations in this paper.Compared with COM,the modified observation model（MOM）further considers the difference between the real user position and the position calculated by SINS.To verify the advantages of this model,INS/GPS integrated navigation simulation experiments are conducted with the usage of COM and MOM respectively.According to the simulation results,the positions（velocities）calculated using COM are divergent over time while the others using MOM are convergent,which demonstrates the higher linearization accuracy of MOM.