Unequal-interval data fusion algorithm for inertial/gravity matching integrated navigation system

Inertial/gravity matching integrated navigation system can effectively improve the longendurance navigation ability of underwater vehicles.Through the analysis of the matching process,the problem of unequal-interval in matching trajectory is addressed by an unequal-interval data fusion algorithm which is based on the unequal-interval characteristics analysis of the matching trajectory.Compared with previously available methods,the proposed algorithm improves the location precision.In conclusion,simulations of the integrated navigation system demonstrated the effectiveness and superiority of the proposed algorithm.

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.

基于多解分离的GNSS/Inertial组合系统完好性监测

在卫星导航系统的完好性监测中,通常的接收机自主完好性监测过分依赖于可见星的几何分布,并且不能提供足够的可用性。针对该问题,给出了一种基于多解分离的惯性辅助卫星导航完好性监测方法。在对基于奇偶矢量的接收机自主完好性监测法及其可用性分析基础上,设计了基于多解分离的卫星导航/惯性组合导航紧耦合滤波器,推导了其完好性监测以及可用性计算方法。通过仿真实验,从可用性、阶跃故障和斜坡故障的完好性监测效果等方面,对两种方法进行了对比分析。结果表明,多解分离法在5颗星时仍能定位故障,水平保护限值可以降低50 m,对于1 m/s的斜坡故障检测时间可以缩短71 s,显著提高了完好性监测性能。

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.

Tightly-coupled model for INS/WSN integrated navigation based on Kalman filter

Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightly-coupled integration based on the Kalman filter （KF）. When the WSN is available, the difference between the distances from the blind node（BN） to the reference nodes （RNs） measured by the INS and those measured by the WSN are used as measurement information for the KF due to its better observability and independence, which can effectively improve the accuracy of the KF. Simulations show that the proposed approach reduces the mean error of the position by about 50% compared with loosely-coupled integration, while the mean error of the velocity is a little higher than that of loosely-coupled integration.

SINS/CNS/GPS integrated navigation algorithm based on UKF

A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

A tightly coupled rotational SINS/CNS integrated navigation method for aircraft

Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated navigation can be divided into two integrated modes:loosely coupled integrated navigation and tightly coupled integrated navigation.Because the loosely coupled SINS/CNS integrated system is only available in the condition of at least three stars,the latter one is becoming a research hotspot.One major challenge of SINS/CNS integrated navigation is obtaining a high-precision horizon reference.To solve this problem,an innovative tightly coupled rotational SINS/CNS integrated navigation method is proposed.In this method,the rotational SINS error equation in the navigation frame is used as the state model,and the starlight vector and star altitude are used as measurements.Semi-physical simulations are conducted to test the performance of this integrated method.Results show that this tightly coupled rotational SINS/CNS method has the best navigation accuracy compared with SINS,rotational SINS,and traditional tightly coupled SINS/CNS integrated navigation method.

An INS/GNSS integrated navigation in GNSS denied environment using recurrent neural network

In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

Anomalous Geomagnetic Field Modeling for Crustal Structure of a Graben in West Siberia

An intensive magnetic anomaly within the limits of West Siberia Danilov graben-rift indicates magnetic rocks while numerous wells encountered only weakly magnetized Triassic basalts and liparites in the basement covered by thick loose Jurassic and younger sediments. The wells penetrated only the first tens meters of the basement and could not tell us about the liparites structure at depth where supposedly they may form a big single body and magnetic rocks may be situated deeper. Geological ideas on a graben-rift structure may be proved (or rejected) by a computer modeling of its magnetic properties. For the anomalous geomagnetic field interpretation, a method of volume integral equations taking into account demagnetization effect was employed. To fit a model a trial-and-error procedure was utilized. The results show that 1) at the depth some rocks are magnetized in opposite direction to the present field;2) highly magnetized rocks (magnetic susceptibility 0.06 - 0.1SI) coming up continuously from the bottom of the model and situated under the graben;3) the studied structure is not a graben but the rift because the continental light crust is absent.

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

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.

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.

Moderate Geomagnetic Storm Condition,WAAS Alerts and Real GPS Positioning Quality

The most significant part of Wade Area Augmentation System (WAAS) integrity consists of the User Differential Range Error (UDRE) and the Grid Ionospheric Vertical Error (GIVE). WAAS solutions are not completely appropriate to determine the GIVE term within the entire coverage zone taking in account real irregular structure of the ionosphere. It leads to the larger confidence bounding terms and lower expected positioning availability in comparison to the reality under geomagnetic storm conditions and system outages. Thus a question arises: is the basic WAAS concept appropriate to provide the same efficiency of the integrity monitoring for both “global differential correction”(i.e. clock, ephemeris et al.) and “local differential correction”(i.e. ionosphrere, troposhpere and multipath)? The aim of this paper is to compare official WAAS integrity monitoring reports and real positioning quality in US coverage zone (CONUS) and Canada area under geomagnetic storm con-ditions and system outages. In this research we are interested in compari-son between real GPS positioning quality based on single-frequency C/A ranging mode and HAL (VAL) values which correspond to the LP, LPV and LPV200 requirements. Significant mismatch of the information be-tween WAAS integrity data and real positioning quality was unfolded as a result of this comparison under geomagnetic storms and system outages on February 14, 2011 and June 22, 2015. Based on this result we think that in order to achieve high confidence of WAAS positioning availability alerts real ionospheric measurements within the wide area coverage zone must be involved instead of the WAAS GIVE values. The better way to realize this idea is to combine WAAS solutions to derive “global differential cor-rections” and LAAS solutions to derive “local differential corrections”.

An Alternative Method to Study Wave Scattering by Semi-infinite Inertial Surfaces

A new method to solve the boundary value problem arising in the study of scattering of two-dimensional surface water waves by a discontinuity in the surface boundary conditions is presented in this paper. The discontinuity arises due to the floating of two semi-infinite inertial surfaces of different surface densities. Applying Green＇s second identity to the potential functions and appropriate Green＇s functions, this problem is reduced to solving two coupled Fredholm integral equations with regular kernels. The solutions to these integral equations are used to determine the reflection and the transmission coefficients. The results for the reflection coefficient are presented graphically and are compared to those obtained earlier using other research methods. It is observed from the graphs that the results computed from the present analysis match exactly with the previous results.

直升机整体式惯性粒子分离器研究现状

整体式惯性粒子分离器是一种直升机进气防护装置,其性能优异、应用广泛,对于保护直升机发动机、延长其使用寿命具有重要意义。本文从4个方面对整体式惯性粒子分离器的研究现状进行归纳总结,包括:总结了现有直升机进气防护装置的类型及相关气动参数,梳理了整体式惯性粒子分离器现有的研究方法及取得的相关成果,分析了影响整体式惯性粒子分离器工作性能的因素,并展望了整体式惯性粒子分离器未来的发展方向。研究成果可为今后开展整体式惯性粒子分离器研究及优化设计提供一定参考。

一种基于深度学习辅助的SINS/DVL组合导航方法

水下机器人(autonomous underwater vehicle,AUV)导航定位精度一定上程度影响了AUV的工作效率,由于GNSS无法在水下使用,以捷联惯导系统/多普勒计程仪(SINS/DVL)的组合导航系统受到众多青睐。DVL在部分情况下会失效,若直接将DVL隔离,系统将变为纯惯性导航系统,严重影响导航定位的精度。为了应对DVL在部分波束缺失的情况,提出了一种DLinear-informer辅助组合导航算法。通过DLinear对原始输入数据特有的分解方式,增强了算法对AUV非线性信息的提取与学习,提高了速度预测精度。实验结果表明:所提算法能够准确预测DVL失效期间丢失波束的速度,降低组合导航的位置误差,提高了系统的鲁棒性和定位精度。

轨迹发生器的研究进展与展望

轨迹发生器是对惯性导航系统及其组合导航系统进行算法研究和仿真验证的重要工具之一。首先,通过梳理分析轨迹发生器在不同运载体的研究现状,概述理想型、数值算法型、数学模型型轨迹发生器的设计方法与主要思路;然后,归纳研究重点,对3类轨迹发生器进行仿真对比,分析3类轨迹发生器的特性和问题;最后,对轨迹发生器下一步的研究进行展望,为该领域的研究提供一定的思路。