scholarly journals Dual-Satellite Alternate Switching Ranging/INS Integrated Navigation Algorithm for Broadband LEO Constellation Independent of Altimeter and Continuous Observation

2021 ◽  
Vol 13 (16) ◽  
pp. 3312
Author(s):  
Lvyang Ye ◽  
Yikang Yang ◽  
Xiaolun Jing ◽  
Hengnian Li ◽  
Haifeng Yang ◽  
...  
Keyword(s):  
Real Time ◽  
Unscented Kalman Filter ◽  
State Equation ◽  
Low Earth Orbit ◽  
Integrated Navigation ◽  
Continuous Observation ◽  
Noise Interference ◽  
Location Service ◽  
Navigation Algorithm ◽  
Alternate Switching

In challenging environments such as forests, valleys and higher latitude areas, there are usually fewer than four visible satellites. For cases with only two visible satellites, we propose a dual-satellite alternate switching ranging integrated navigation algorithm based on the broadband low earth orbit (LEO) constellation, which integrates communication and navigation (ICN) technology. It is different from the traditional dual-satellite integrated navigation algorithm: the difference is that it can complete precise real-time navigation and positioning without an altimeter and continuous observation. First, we give the principle of our algorithm. Second, with the help of an unscented Kalman filter (UKF), we give the observation equation and state equation of our algorithm, and establish the mathematical model of multipath/non-line of sight (NLOS) and noise interference. Finally, based on the SpaceX constellation, for various scenarios, we analyze the performance of our algorithm through simulation. The results show that: our algorithm can effectively suppress the divergence of the inertial navigation system (INS), in the face of different multipath/NLOS interference and various noise environments it still keeps good robustness, and also has great advantages in various indicators compared with the traditional dual-satellite positioning algorithms and some existing 3-satellite advanced positioning algorithms. These results show that our algorithm can meet the real-time location service requirements in harsh and challenging environments, and provides a new navigation and positioning method when there are only two visible satellites.

scholarly journals Altimeter + INS/Giant LEO Constellation Dual-Satellite Integrated Navigation and Positioning Algorithm Based on Similar Ellipsoid Model and UKF

2021 ◽  
Vol 13 (20) ◽  
pp. 4099
Author(s):  
Lvyang Ye ◽  
Yikang Yang ◽  
Xiaolun Jing ◽  
Hengnian Li ◽  
Haifeng Yang ◽  
...  
Keyword(s):  
Satellite Communication ◽  
Unscented Kalman Filter ◽  
Low Earth Orbit ◽  
Integrated Navigation ◽  
Communication Services ◽  
Location Service ◽  
Switching Algorithm ◽  
Navigation Algorithm ◽  
Frequent Switching ◽  
Positioning Algorithm

To solve the problem of location service interruption that is easily caused by incomplete visual satellite environments such as occlusion, urban blocks and mountains, we propose an altimeter + inertial navigation system (INS) + giant low earth orbit (LEO) dual-satellite (LEO2) switching integrated navigation algorithm based on a similar ellipsoid model and unscented Kalman filter (UKF). In addition to effectively improving the INS error, for the INS + LEO dual-satellite switching algorithm without altimeter assistance, our algorithm can also significantly suppress the problem of excessive navigation and positioning error caused by this algorithm in a long switching time, it does not require frequent switching of LEO satellites, and can ensure navigation and positioning functions without affecting LEO satellite communication services. In addition, the vertical dilution of precision (VDOP) value can be improved through the clock error elimination scheme, so, the vertical accuracy can be improved to a certain extent. For different altimeter deviations, we provide simulation experiments under different altimeter deviations; it can be found that after deducting the fixed height deviation, the algorithm can also achieve good accuracy. Compared with other typical algorithms, our proposed algorithm has higher accuracy, lower cost and stronger real-time performance, and is suitable for navigation and positioning scenarios in harsh environments.

scholarly journals Single-Satellite Integrated Navigation Algorithm Based on Broadband LEO Constellation Communication Links

2021 ◽  
Vol 13 (4) ◽  
pp. 703
Author(s):  
Lvyang Ye ◽  
Yikang Yang ◽  
Xiaolun Jing ◽  
Jiangang Ma ◽  
Lingyu Deng ◽  
...  
Keyword(s):  
Real Time ◽  
Satellite Communication ◽  
The State ◽  
Earth Orbit ◽  
Integrated Navigation ◽  
Location Services ◽  
The Real ◽  
Navigation Algorithm ◽  
Communication Links ◽  
Leo Satellite

With the rapid development of satellite technology and the need to satisfy the increasing demand for location-based services, in challenging environments such as indoors, forests, and canyons, there is an urgent need to improve the position accuracy in these environments. However, traditional algorithms obtain the position solution through time redundancy in exchange for spatial redundancy, and they require continuous observations that cannot satisfy the real-time location services. In addition, they must also consider the clock bias between the satellite and receiver. Therefore, in this paper, we provide a single-satellite integrated navigation algorithm based on the elimination of clock bias for broadband low earth orbit (LEO) satellite communication links. First, we derive the principle of LEO satellite communication link clock bias elimination; then, we give the principle and process of the algorithm. Next, we model and analyze the error of the system. Subsequently, based on the unscented Kalman filter (UKF), we model the state vector and observation vector of our algorithm and give the state and observation equations. Finally, for different scenarios, we conduct qualitative and quantitative analysis through simulations, and the results show that, whether in an altimeter scenario or non-altimeter scenario, the performance indicators of our algorithm are significantly better than the inertial navigation system (INS), which can effectively overcome the divergence problem of INS; compared with the medium earth orbit (MEO) constellation, the navigation trajectory under the LEO constellation is closer to the real trajectory of the aircraft; and compared with the traditional algorithm, the accuracy of each item is improved by more than 95%. These results show that our algorithm not only significantly improves the position error, but also effectively suppresses the divergence of INS. The algorithm is more robust and can satisfy the requirements of cm-level real-time location services in challenging environments.

scholarly journals An INS/Geomagnetic Integrated Navigation Algorithm Based on Matching Strategy and Hierarchical Filtering

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 460
Author(s):  
Xiusheng Duan ◽  
Jing Xiao ◽  
Xiaohui Qi ◽  
Yifei Liu
Keyword(s):  
Magnetic Measurements ◽  
Inertial Navigation ◽  
State Equation ◽  
Integrated Navigation ◽  
Matching Algorithm ◽  
Filtering Algorithm ◽  
Navigation Algorithm ◽  
Physical Experiments ◽  
Matching Strategy ◽  
Navigation Information

To improve the positioning accuracy of an inertial/geomagnetic integrated navigation algorithm, a combined navigation method based on matching strategy and hierarchical filtering is proposed. First, the PDA-ICCP geomagnetic matching algorithm is improved. On basis of evaluating the distribution of magnetic measurements, a number of controllable magnetic values are regenerated to participate in the geomagnetic matching algorithm (GMA). As a result, accuracy of the matching algorithm is ensured and its efficiency is improved. Secondly, the integrated navigation filter is designed based on the hierarchical filtering strategy, in which the navigation information of the geomagnetic matching module and inertial navigation module are respectively filtered and fused in the main filter. In this way, the shortcoming that GMA is unable to provide continuous and real-time navigation information is overcome. Meanwhile, precision of the inertial/geomagnetic integrated navigation algorithm is improved. Finally, the feasibility and validity of the proposed algorithm are verified by simulation and physical experiments. Compared with the integrated filtering algorithm which directly uses the error equation of inertial navigation system (INS) as the state equation, the proposed hierarchical filtering algorithm can achieve higher positioning precision.

Autonomous navigation algorithm based on AUKF filter about fusion of geomagnetic and sunlight directions

2018 ◽  
Vol 11 (4) ◽  
pp. 471-485 ◽  
Author(s):  
Bing Hua ◽  
Zhiwen Zhang ◽  
Yunhua Wu ◽  
Zhiming Chen
Keyword(s):  
Orbit Determination ◽  
Geomagnetic Field ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Field Vector ◽  
Low Earth Orbit ◽  
Fusion Algorithm ◽  
Content Type ◽  
Navigation Algorithm

Purpose The geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector measured by on board three-axis magnetometer with the standard value of the international geomagnetic field. The geomagnetic model has the disadvantages of uncertainty, low precision and long-term variability. Therefore, accuracy of autonomous navigation using the magnetometer is low. The purpose of this paper is to use the geomagnetic and sunlight information fusion algorithm to improve the orbit accuracy. Design/methodology/approach In this paper, an autonomous navigation method for low earth orbit satellite is studied by fusing geomagnetic and solar energy information. The algorithm selects the cosine value of the angle between the solar light vector and the geomagnetic vector, and the geomagnetic field intensity as observation. The Adaptive Unscented Kalman Filter (AUKF) filter is used to estimate the speed and position of the satellite, and the simulation research is carried out. This paper also made the same study using the UKF filter for comparison with the AUKF filter. Findings The algorithm of adding the sun direction vector information improves the positioning accuracy compared with the simple geomagnetic navigation, and the convergence and stability of the filter are better. The navigation error does not accumulate with time and has engineering application value. It also can be seen that AUKF filtering accuracy is better than UKF filtering accuracy. Research limitations/implications Geomagnetic navigation is greatly affected by the accuracy of magnetometer. This paper does not consider the spacecraft’s environmental interference with magnetic sensors. Practical implications Magnetometers and solar sensors are common sensors for micro-satellites. Near-Earth satellite orbit has abundant geomagnetic field resources. Therefore, the algorithm will have higher engineering significance in the practical application of low orbit micro-satellites orbit determination. Originality/value This paper introduces a satellite autonomous navigation algorithm. The AUKF geomagnetic filter algorithm using sunlight information can obviously improve the navigation accuracy and meet the basic requirements of low orbit small satellite orbit determination.

scholarly journals Orbit Propagation and Determination of Low Earth Orbit Satellites

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ho-Nien Shou
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Nonlinear Problems ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Positioning Accuracy ◽  
Orbit Propagation

This paper represents orbit propagation and determination of low Earth orbit (LEO) satellites. Satellite global positioning system (GPS) configured receiver provides position and velocity measures by navigating filter to get the coordinates of the orbit propagation (OP). The main contradictions in real-time orbit which is determined by the problem are orbit positioning accuracy and the amount of calculating two indicators. This paper is dedicated to solving the problem of tradeoffs. To plan to use a nonlinear filtering method for immediate orbit tasks requires more precise satellite orbit state parameters in a short time. Although the traditional extended Kalman filter (EKF) method is widely used, its linear approximation of the drawbacks in dealing with nonlinear problems was especially evident, without compromising Kalman filter (unscented Kalman Filter, UKF). As a new nonlinear estimation method, it is measured at the estimated measurements on more and more applications. This paper will be the first study on UKF microsatellites in LEO orbit in real time, trying to explore the real-time precision orbit determination techniques. Through the preliminary simulation results, they show that, based on orbit mission requirements and conditions using UKF, they can satisfy the positioning accuracy and compute two indicators.

MEMS-SINS/GPS/Magnetometer Integrated Navigation System for Small Unmanned Aerial Vehicles

2014 ◽  
Vol 568-570 ◽  
pp. 976-986 ◽  
Author(s):  
Cun Xiao Miao ◽  
Juan Juan Cao ◽  
Yang Bin Ou
Keyword(s):  
Low Power ◽  
Real Time ◽  
Navigation System ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Flight Test ◽  
Integrated Navigation ◽  
Autonomous Flight ◽  
Integrated Navigation System ◽  
Small Uav

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

scholarly journals Tightly Coupled Integrated Navigation Algorithm for Hypersonic Boost-Glide Vehicles in the LCEF Frame

Aerospace ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 124
Author(s):  
Kai Chen ◽  
Sensen Pei ◽  
Fuqiang Shen ◽  
Shangbo Liu
Keyword(s):  
Inertial Navigation ◽  
State Equation ◽  
Integrated Navigation ◽  
Loosely Coupled ◽  
Navigation Algorithm ◽  
Tightly Coupled ◽  
Measurement Equations ◽  
Range Rate ◽  
Better Than ◽  
Strapdown Inertial Navigation

According to the trajectory characteristics of hypersonic boost-glide vehicles, a tightly coupled integrated navigation algorithm for hypersonic vehicles based on the launch-centered Earth-fixed (LCEF) frame is proposed. First, the strapdown inertial navigation mechanization algorithm and discrete update algorithm in the LCEF frame are introduced. Subsequently, the attitude, velocity, and position error equations of strapdown inertial navigation in the LCEF frame are introduced. The strapdown inertial navigation system/global positioning system (SINS/GPS) pseudo-range and pseudo-range rate measurement equations in the LCEF frame are derived. Further, the tightly coupled SINS/GPS integrated navigation filter state equation and the measurement equation are presented. Finally, the tightly coupled SINS/GPS integrated navigation algorithm is verified in the hardware-in-the-loop (HWIL) simulation environment. The simulation results indicate that the precision of tightly coupled integrated navigation is better than that of loosely coupled integrated navigation. Moreover, even when the number of effective satellites is less than four, tightly coupled integrated navigation functions well, thus verifying the effectiveness and feasibility of the algorithm.

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

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