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

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.

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

Remote Sensing ◽

10.3390/rs13163312 ◽

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.

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

Remote Sensing ◽

10.3390/rs13040703 ◽

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.

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

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-07-2017-0087 ◽

2018 ◽

Vol 11 (4) ◽

pp. 471-485 ◽

Cited By ~ 1

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.

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Indoor and Outdoor Low-Cost Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System

Remote Sensing ◽

10.3390/rs12193271 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3271

Author(s):

Ningbo Li ◽

Lianwu Guan ◽

Yanbin Gao ◽

Shitong Du ◽

Menghao Wu ◽

...

Keyword(s):

Navigation System ◽

Low Cost ◽

Dead Reckoning ◽

Satellite System ◽

Outdoor Environment ◽

Mode Switching ◽

Integrated Navigation ◽

Switching Algorithm ◽

Navigation Algorithm ◽

Indoor And Outdoor

Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.

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SINS/CNS Nonlinear Integrated Navigation Algorithm for Hypersonic Vehicle

Mathematical Problems in Engineering ◽

10.1155/2015/903054 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Yong-jun Yu ◽

Jin-fa Xu ◽

Zhi Xiong

Keyword(s):

Navigation System ◽

Nonlinear Filtering ◽

Inertial Navigation System ◽

Hypersonic Vehicle ◽

Measurement Information ◽

Horizontal Position ◽

Integrated Navigation ◽

Navigation Algorithm ◽

Celestial Navigation ◽

Positioning Algorithm

Celestial Navigation System (CNS) has characteristics of accurate orientation and strong autonomy and has been widely used in Hypersonic Vehicle. Since the CNS location and orientation mainly depend upon the inertial reference that contains errors caused by gyro drifts and other error factors, traditional Strap-down Inertial Navigation System (SINS)/CNS positioning algorithm setting the position error between SINS and CNS as measurement is not effective. The model of altitude azimuth, platform error angles, and horizontal position is designed, and the SINS/CNS tightly integrated algorithm is designed, in which CNS altitude azimuth is set as measurement information. GPF (Gaussian particle filter) is introduced to solve the problem of nonlinear filtering. The results of simulation show that the precision of SINS/CNS algorithm which reaches 130 m using three stars is improved effectively.

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

Journal of Navigation ◽

10.1017/s0373463320000582 ◽

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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A Method for Autonomous Navigation and Positioning of UAV Based on Electric Field Array Detection

Sensors ◽

10.3390/s21041146 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1146 ◽

Cited By ~ 1

Author(s):

Yincheng Li ◽

Wenbin Zhang ◽

Peng Li ◽

Youhuan Ning ◽

Chunguang Suo

Keyword(s):

Mathematical Model ◽

Power Systems ◽

Transmission Lines ◽

Autonomous Navigation ◽

Strength Distribution ◽

Field Source ◽

Navigation Algorithm ◽

Safety Warning ◽

Positioning Algorithm ◽

Navigation Equipment

At present, the method of using unmanned aerial vehicles (UAVs) with traditional navigation equipment for inspection of overhead transmission lines has the limitations of expensive sensors, difficult data processing, and vulnerable to weather and environmental factors, which cannot ensure the safety of UAV and power systems. Therefore, this paper establishes a mathematical model of spatial distribution of transmission lines to study the field strength distribution information around transmission lines. Based on this, research the navigation and positioning algorithm. The data collected by the positioning system are input into the mathematical model to complete the identification, positioning, and safety distance diagnosis of the field source. The detected data and processing results can provide reference for UAV obstacle avoidance navigation and safety warning. The experimental results show that the positioning effect of the positioning navigation algorithm is obvious, and the positioning error is within the range of use error and has good usability and application value.

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Research on outdoor AGV precise navigation based on BDS/INS data fusion

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189690 ◽

2021 ◽

pp. 1-12

Author(s):

Yongwei Tang ◽

Huijuan Hao ◽

Jun Zhou ◽

Yuexiang Lin ◽

Zhenzhen Dong

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Navigation System ◽

Integrated Navigation ◽

Complex Environments ◽

Assisted Navigation ◽

Precise Control ◽

Bee Colony ◽

Navigation Algorithm ◽

Navigation Error

AGV (Automated Guided Vehicle) technology has attracted increasing attention. Precise control of AGV position and attitude information in complex operating environment is a key part of smart factories. With outdoor AGV as a platform, this study uses BDS/INS combined navigation system combining Beidou positioning system and inertial navigation system and takes the velocity and position difference between BDS and INS as a model. An integrated navigation method is proposed to improve bee colony algorithm and optimize the BP neural network-assisted Kalman filtering to achieve accurate positioning. Moreover, the optimization of BP neural network navigation using INS navigation, network-assisted navigation and bee colony algorithm is simulated. Results demonstrate that the integrated navigation algorithm has effectiveness and feasibility, and can solve the problems of BDS misalignment and large INS navigation error in complex environments.

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