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.

A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

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.

An Integrated Air Data / GPS Navigation System for Helicopters

In this study, the integration of two navigation systems Air Data System (ADS) and Global Positioning System (GPS) was aimed. ADS is a widely used navigation system which measures static and total air pressure and the air temperature. ADS has high sampling frequency and poor accuracy, on the other hand, another navigation system GPS has high accuracy compared to ADS but lower sampling frequency.Kalman Filter is used to integrate and minimize the errors of the two navigation systems. By this integration a navigation system with high sampling frequency and high accuracy is aimed. Another object is to calculate the wind speed with high accuracy.

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.

Integrated Navigation Filtering Method Based on Wavelet Neural Network Optimized by MEA Model

In the experiment of combined navigation filtering using wavelet neural network, the initial parameters of the network have the influence of randomness on network convergence and navigation accuracy. A combined navigation filtering method based on wavelet neural network optimized by mind evolution algorithm is proposed. Firstly, the efficient global search ability of the mind evolution algorithm was used to quickly and accurately obtain the initial parameters of the appropriate wavelet neural network, and then the optimized wavelet neural network was applied to directly predict the position and velocity error data. This method is different from the traditional filtering method, while avoiding the drawbacks of the neural network. The simulation experiments with wavelet neural network and GA-wavelet network were carried out. The results show that the proposed method can effectively improve the accuracy of the integrated navigation system and provide a feasible path for combined navigation filtering.

A Map-Matching Algorithm forGPS/DR Integrated Navigation Systems Basedon Dempster-Shafer Evidence Reasoning

GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and the unbounded DR accumulation of errors over time make the position information with undesirable position errors. The map matching can improve the position accuracy and availability of the vehicular position system. In this paper, general principle of map matching is investigated according to segmentation and feature extraction, and a map matching algorithm based on D-S (Dempster-Shafer) evidence reasoning for GPS integrated navigation system is proposed, which can find the exact road on which a car moves. For the experiments, a car navigation system is developed with some sensors and the field test demonstrates the effectiveness and applicability of the algorithm for the car location and navigation.

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.

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.

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.

冬小麦-夏玉米周年“四密一稀”浅埋滴灌水肥药一体化绿色生产技术

本研究针对黄淮海冬小麦-夏玉米两熟区面临的出苗质量、播种—出苗期水分供应、灌溉施肥及时性及技术粗放等问题,并就进一步提高光温水肥资源利用效率是实现该区域粮食单产提升和绿色发展的重要途径等,从2018年起开展了一系列创新研究。通过实施冬小麦“四密一稀”条带种植配套卫星导航播种技术、冬小麦-夏玉米周年浅埋滴灌水肥药一体化技术等关键核心技术,结合配套现代化农机与信息技术装备,形成了一套集成的“冬小麦-夏玉米周年‘四密一稀’浅埋滴灌水肥药一体化绿色生产技术”。2010—2023年田间示范表明,该技术有效解决了上述生产难题,达到了小麦玉米周年增产和水肥资源高效利用的目标。与农民传统相比,小麦增产9%~17%,玉米增产12%~14%,周年节水450~750 m^(3)/hm^(2),节肥20%,节约人工成本2250~3000元/hm^(2)。该技术为黄淮海区冬小麦-夏玉米周年绿色高产提供了新的可行性方案。

Research on the Relationship Between Information Fusion Method and Information Failure Mode in Integrated Navigation System

On the basis of the basic principles of weighted fusion, Kalman filtering and BP neural networks, the basic principles of information fusion methods used in integrated navigation systems are expounded. Through the analysis of the basic principles, the as-sociation of information fusion methods commonly used in integrated navigation systems and information failure modes is obtained: the information fault mode of weighted fusion method The model is closely related to the specific weight allocation method, which depends on the fault mode of the sensor or sub-system in which the weight is dominant;the information fault mode of the Kalman filtering information fusion method is a continuous mutation fault corresponding to the nonlinear time interval of the system;the in-formation fault mode of the BP neural network method is gradual with time. The information failure mode of the BP neural network method is a slowly varying fault that gradually accumulates over time. Starting from the complexity associated with the information fusion method and the information failure mode, it is pointed out that in order to systematically express the relationship between the information fusion method and the information failure mode, further research can be carried out.

基于STM32的智能航行系统设计

为解决航行器智能确定航向并航行的问题,本文采取模块化的思想设计了一款基于STM32的红外导引的智能航行控制系统。该智能航行系统利用红外接收模块进行红外导引信号的接收,采取线性加权法进行航向与导引信号之间的偏离角确定,利用比例-积分-微分(proportion-integration-differentiation,PID)控制算法对控制舵机转角的脉冲宽度调制(pulse width modulation,PWM)信号的占空比进行确定,从而对螺旋桨的转速与舵桨的转角进行可靠控制。根据水池测试结果,该智能航行控制系统能够对红外接收信号进行快速处理,在保持较高航速的条件下进行可靠的航行控制。

基于系统动力学模型的综合货运枢纽绿色转型机理及实施路径研究

为助力加快实现交通运输行业绿色转型,以综合货运枢纽为着眼点,探索其绿色转型和高质量发展的实施方案。首先,识别出政策、技术创新、管理效率等3个关键因素,揭示综合货运枢纽绿色转型的动态支撑机理。然后,基于对各因素的分析,将环保政策松紧系数、科技投入系数、污染物排放系数作为变量,构建包含枢纽物流供应子系统、枢纽物流需求子系统、节能减排子系统的系统动力学模型。再以四川省某综合货运枢纽为例进行模拟,验证模型的有效性和实用性。最后,从政策标准、技术组合和管理效率等3个方面提出绿色转型路径。研究结果显示:(1)表征政策因素的环保政策松紧系数对节能减排有显著正面影响,但同时限制了枢纽总产值的提升,需关注政策出台时机和松紧度;(2)表征科技研发投入的科技投入系数对节能减排有显著正面影响,既减少碳排放,又不影响枢纽总产值提升,且存在后发优势;(3)表征绿色技术设备投入效果的污染物排放系数对节能减排有显著正面影响,但提示资金投入与减排效果并不是简单的线性关系,需重点关注绿色技术设备的组合优化,做好技术与业务流程的融合;(4)表征管理效率的排放治理投资系数呈现先增后减的趋势,说明需在枢纽结构、招商、人力、资金等方面设定合理区间以确保环保投入产出效果。通过动态模拟综合货运枢纽绿色转型机理,为地方政府因地制宜出台相关政策,以及为货运枢纽运营企业采取科学有效的技术手段和管理措施提供了理论依据。

Intelligent fault-tolerant algorithm with two-stage and feedback for integrated navigation federated filtering

In order to take full advantage of federated filter in fault-tolerant design of integrated navigation system, the limitation of fault detection algorithm for gradual changing fault detection and the poor fault tolerance of global optimal fusion algorithm are the key problems to deal with. Based on theoretical analysis of the influencing factors of federated filtering fault tolerance, global fault-tolerant fusion algorithm and information sharing algorithm are proposed based on fuzzy assessment. It achieves intelligent fault-tolerant structure with two-stage and feedback, including real-time fault detection in sub-filters, and fault-tolerant fusion and information sharing in main filter. The simulation results demonstrate that the algorithm can effectively improve fault-tolerant ability and ensure relatively high positioning precision of integrated navigation system when a subsystem having gradual changing fault.

Robust adaptive UKF based on SVR for inertial based integrated navigation

Aiming at the problem that the traditional Unscented Kalman Filtering(UKF) algorithm can't solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers,this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression(SVR).The algorithm combines the advantages of support vector regression with small samples,nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation.Firstly,the SVR model is trained by using the innovation in the sliding window,and the new innovation is monitored.If the deviation between the estimated innovation and the measured innovation exceeds a given threshold,then measured innovation will be replaced by the predicted innovation,and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm.Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF,robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.

Indoor Navigation Network Model Construction Method Based on Building Information Model

In view of the poor information integrity of the 3D model used to make the indoor road network and the lack of versatility of the constructed indoor road network, a method for building an indoor navigation network model that can be seamlessly connected with outdoor paths is proposed in this paper. First, the IFC model is converted to the CityGML model using the BIM model as the indoor data source. Then, using GIS technology and limited Delaunay triangulation refinement algorithm, the necessary elements of indoor navigate on network model such as semantic information, geometric information and topological relationship contained in CityGML model are extracted. Finally, it is visualized and verified based on experimental model data. The results show that the indoor navigation network model constructed based on the CityGML model can accurately perform indoor navigation, make the constructed road network more general, and provide reference and technical support for the integrated construction of indoor and outdoor road network models.

航标公共网络系统建设的研究

把不同数据模型、不同技术路线和软硬件平台异构的各助航服务信息系统整合起来并通过公共网络对外发布是助航服务发展的必然趋势。本文以东海海区航标信息化建设现状及发展趋势为背景,论述了航标公共网络系统建设的目标、意义,对航标公共网络系统的架构、网络拓扑结构、工作内容及实现系统功能所需的关键技术进行了初步研究。

基于轨道不平顺匹配的多源融合列车定位方法

列车定位是保障铁路安全高效运营的基础,面向先进列车运行控制系统高精度、连续、可靠的位置服务需求,研究高精度、高可靠、低成本的匹配定位源提升列车定位系统的稳健性和准确度具有重要的现实意义。本文提出一种无布设轨道特征信号-轨道不平顺,利用惯性导航系统感知和测量轨道不平顺信号实现匹配定位,结合列车运动约束,实现了无布设情况下的全自主、全天候、全地域的连续可靠的组合定位。为了验证该方案的可行性,本文在真实铁路线路上进行了列车车载试验。试验结果表明:一个典型的战术级IMU可以实现0.6 m(98.46%置信度)的列车定位精度。该方法为多传感器融合的列车定位系统提供了一种新的定位信号源,可以用于提高车载列车定位系统的准确性和稳健性。