scholarly journals An Autonomous Navigation Algorithm for High Orbit Satellite Using Star Sensor and Ultraviolet Earth Sensor

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Li Baohua ◽  
Lai Wenjie ◽  
Chen Yun ◽  
Liu Zongming
Keyword(s):  
Coordinate System ◽  
Autonomous Navigation ◽  
Optical Axis ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Integrated Sensor ◽  
Axis Direction ◽  
Navigation Data ◽  
Navigation Algorithm ◽  
Center Vector

An autonomous navigation algorithm using the sensor that integrated the star sensor (FOV1) and ultraviolet earth sensor (FOV2) is presented. The star images are sampled by FOV1, and the ultraviolet earth images are sampled by the FOV2. The star identification algorithm and star tracking algorithm are executed at FOV1. Then, the optical axis direction of FOV1 at J2000.0 coordinate system is calculated. The ultraviolet image of earth is sampled by FOV2. The center vector of earth at FOV2 coordinate system is calculated with the coordinates of ultraviolet earth. The autonomous navigation data of satellite are calculated by integrated sensor with the optical axis direction of FOV1 and the center vector of earth from FOV2. The position accuracy of the autonomous navigation for satellite is improved from 1000 meters to 300 meters. And the velocity accuracy of the autonomous navigation for satellite is improved from 100 m/s to 20 m/s. At the same time, the period sine errors of the autonomous navigation for satellite are eliminated. The autonomous navigation for satellite with a sensor that integrated ultraviolet earth sensor and star sensor is well robust.

An Autonomous Navigation Algorithm Using Geomagnetic Sensor and Star Sensor

2013 ◽  
Vol 650 ◽  
pp. 449-454
Author(s):  
Cai Min Chen ◽  
Bao Hua Li ◽  
Chang Hong Wang ◽  
Rui Liu
Keyword(s):  
Autonomous Navigation ◽  
Singular Value ◽  
Dynamic Equations ◽  
Star Sensor ◽  
Singular Value Decomposition Method ◽  
Navigation Algorithm ◽  
Value Decomposition ◽  
Sensor Navigation ◽  
Accumulated Error ◽  
Navigation Method

Aiming at the limitations of the orbital dynamic equations based star sensor navigation method, a star sensor /geomagnetic information utilized aircraft autonomous navigation method is proposed. Dynamic equations applicable to general aircrafts are established. System observation equations are deduced. The angle between geomagnetic and starlight vector is used as observation in the algorithm. Extended Kalman filter is used to estimate position and velocity of aircraft in the algorithm. Singular value decomposition method is used to analyze observability of the system. Simulation results show that the algorithm has many advantages including high precision, good filtering convergence and stability, and non-accumulated error. The algorithm can be used as aided navigation of inertial navigation or in occasions, which only require a general navigation precision.

Research on the algorithm of solving the optical axis direction of celestial navigation star sensor

2021 ◽  
Author(s):  
Zhe Wen ◽  
Hongwei Bian ◽  
Rongying Wang ◽  
Heng Ma ◽  
Zhonglei Zhu
Keyword(s):  
Optical Axis ◽  
Star Sensor ◽  
Axis Direction ◽  
Celestial Navigation

The Stellar-INS Navigation Performance Influence Mechanism of Star Vector Orientation in the Field of View

2020 ◽  
pp. 1-13
Author(s):  
Chunxi Zhang ◽  
Yanqiang Yang ◽  
Hao Zhang ◽  
Xiaowen Cai
Keyword(s):  
Error Estimation ◽  
Optical Axis ◽  
Inertial Navigation ◽  
Field Of View ◽  
Estimation Accuracy ◽  
Large Field ◽  
Star Sensor ◽  
Axis Direction ◽  
Influence Mechanism ◽  
Vector Orientation

The star sensor field of view varies from several arc-minutes to 20 degrees, which directly determines the star vector orientation in the field of view (FOV). Although the relationship between star vector orientation in the FOV and attitude accuracy has been revealed, the influence mechanism of star vector orientation on the integrated navigation performance of a stellar inertial navigation system has not been analysed. In order to improve the integrated accuracy, the main errors such as star sensor installation error, gyro error and initial platform angle error should be estimated online. It is significant to study the influence mechanism of star vector orientation on estimation of the above errors. In this paper, the star sensor sensitivity and the geometry factor are defined to feature the difference between the optical axis direction and the non-optical axis direction. The formulised mechanism and quantification results between star vector orientation and integration attitude and error estimation accuracy are clearly given. Simulation and ground testing were conducted and it was found that the larger the star vector orientation along the optical axis, the better the error estimation accuracy. In contrast, the attitude accuracy is weakly sensitive to the orientation of the star vector in conditions of appropriate posture adjustment and star observation scheme. This conclusion can offer universal guidance for the design and evaluation of stellar inertial navigation systems with narrow field of view or large field of view star sensors.

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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1146 ◽  
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.

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 Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

scholarly journals On-Orbit Calibration of Installation Parameter of Multiple Star Sensors System for Optical Remote Sensing Satellite with Ground Control Points

2020 ◽  
Vol 12 (7) ◽  
pp. 1055
Author(s):  
Yanli Wang ◽  
Mi Wang ◽  
Ying Zhu
Keyword(s):  
Remote Sensing ◽  
Coordinate System ◽  
Statistical Characteristics ◽  
Optical Remote Sensing ◽  
Star Sensor ◽  
Control Points ◽  
Remote Sensing Images ◽  
Multiple Star ◽  
Ground Control Points ◽  
Combination Modes

Owing to the vibrations and thermal shocks that arise during the launch and orbit penetration process, the on-orbit installation parameters of multiple star sensors are different from the on-ground measured parameters, causing inconsistencies in the attitude determinations from different combination modes and seriously affecting the geometric accuracy of high-resolution optical remote sensing images. This study presents an on-orbit calibration approach for the installation parameters of a multiple star sensors system using ground control points (GCPs). Based on the on-ground installation parameters of the optical axes of conventional star sensors, a fiducial coordinate system is proposed as the calibration coordinate system. The installation parameters of the conventional star sensors are calibrated using the statistical characteristics of angles between axes of the star sensor and three fiducial vectors in the J2000 celestial coordinate system. Based on the GCPs, the relative fiducial parameters are calculated, and the installation parameter of unconventional star sensor is then calibrated with the relative fiducial parameters and statistical characteristics of angles. It can be used for high-resolution optical remote sensing satellite measuring with only two star sensors to unify the fiducial coordinate system. The proposed method is tested using simulated data and on-orbit measurement data. The results demonstrate that the proposed method can calibrate the optical axis of the star sensor without the restriction of the accuracy of horizontal axis. Moreover, the star sensor with a large installation angle error can be calibrated well using the proposed approach. The results of attitude determinations from different star sensor combination modes are consistent, and the geometric accuracy of the remote sensing images is significantly improved.

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