A Fast Search-Free Algorithm for Star Sensor Frame Identification by Star Configurations. A Version of Onboard Implementation

2021 ◽  
Vol 29 (3) ◽  
pp. 80-95
Author(s):  
V.V. Barke ◽  
◽  
А.А. Venkstern ◽  
V.A. Kottsov ◽  
A.V. Tavrov ◽  
...  
Keyword(s):  
Feature Space ◽  
Local Features ◽  
Computer Memory ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Relevant Feature ◽  
Fast Search ◽  
Onboard Computer ◽  
Star Configurations

The paper presents a method for identifying the frame of a star sensor (SS), based on determination of the star local features allowing its unique recognition. The star identifiers are located in a multidimensional integer feature space, and the relevant feature catalog presents a disperse array, which provides search-free star determination. Examples of onboard implementation of feature catalog are presented, containing the stars up to magnitude of six. The required memory is estimated, and a method is proposed for compressing the feature catalog to be recorded in the onboard computer memory. The frame identification algorithm using the reduced feature catalog is described in detail. The algorithm was tested on real sky frames.

A Fast Search-Free Algorithm for Star Sensor Frame Identification by Star Configurations. A Version of Onboard Implementation

2021 ◽  
Vol 12 (3) ◽  
pp. 254-264
Author(s):  
V. V. Barke ◽  
A. A. Venkstern ◽  
V. A. Kottsov ◽  
A. V. Tavrov ◽  
A. V. Yudaev
Keyword(s):  
Star Sensor ◽  
Fast Search ◽  
Star Configurations

scholarly journals Efficient Star Identification Using a Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3684
Author(s):  
David Rijlaarsdam ◽  
Hamza Yous ◽  
Jonathan Byrne ◽  
Davide Oddenino ◽  
Gianluca Furano ◽  
...  
Keyword(s):  
Neural Network ◽  
Attitude Determination ◽  
Lightweight Design ◽  
A Priori ◽  
Search Time ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Feature Extraction Method ◽  
Star Identification ◽  
Space Star

The required precision for attitude determination in spacecraft is increasing, providing a need for more accurate attitude determination sensors. The star sensor or star tracker provides unmatched arc-second precision and with the rise of micro satellites these sensors are becoming smaller, faster and more efficient. The most critical component in the star sensor system is the lost-in-space star identification algorithm which identifies stars in a scene without a priori attitude information. In this paper, we present an efficient lost-in-space star identification algorithm using a neural network and a robust and novel feature extraction method. Since a neural network implicitly stores the patterns associated with a guide star, a database lookup is eliminated from the matching process. The search time is therefore not influenced by the number of patterns stored in the network, making it constant (O(1)). This search time is unrivalled by other star identification algorithms. The presented algorithm provides excellent performance in a simple and lightweight design, making neural networks the preferred choice for star identification algorithms.

Precise attitude determination of ship based on star sensor

2012 ◽  
Author(s):  
Bing Liu ◽  
Feng Chen ◽  
Tongshuang Zhang ◽  
Dean Zhong ◽  
Lei Yang ◽  
...  
Keyword(s):  
Attitude Determination ◽  
Star Sensor

scholarly journals Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

2013 ◽  
Vol 371 (1993) ◽  
pp. 20120494 ◽  
Author(s):  
P. Spanos ◽  
P. Elsbernd ◽  
B. Ward ◽  
T. Koenck
Keyword(s):  
Finite Element ◽  
Monte Carlo ◽  
Electrical Properties ◽  
Volume Fraction ◽  
Effective Conductivity ◽  
Numerical Models ◽  
Strain Curve ◽  
Identification Algorithm ◽  
Element Discretization

This paper reviews and enhances numerical models for determining thermal, elastic and electrical properties of carbon nanotube-reinforced polymer composites. For the determination of the effective stress–strain curve and thermal conductivity of the composite material, finite-element analysis (FEA), in conjunction with the embedded fibre method (EFM), is used. Variable nanotube geometry, alignment and waviness are taken into account. First, a random morphology of a user-defined volume fraction of nanotubes is generated, and their properties are incorporated into the polymer matrix using the EFM. Next, incremental and iterative FEA approaches are used for the determination of the nonlinear properties of the nanocomposite. For the determination of the electrical properties, a spanning network identification algorithm is used. First, a realistic nanotube morphology is generated from input parameters defined by the user. The spanning network algorithm then determines the connectivity between nanotubes in a representative volume element. Then, interconnected nanotube networks are converted to equivalent resistor circuits. Finally, Kirchhoff's current law is used in conjunction with FEA to solve for the voltages and currents in the system and thus calculate the effective electrical conductivity of the nanocomposite. The model accounts for electrical transport mechanisms such as electron hopping and simultaneously calculates percolation probability, identifies the backbone and determines the effective conductivity. Monte Carlo analysis of 500 random microstructures is performed to capture the stochastic nature of the fibre generation and to derive statistically reliable results. The models are validated by comparison with various experimental datasets reported in the recent literature.

Pentagram star pattern identification algorithm applied in three-head star sensors

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740046
Author(s):  
Feng Wu ◽  
Xifang Zhu ◽  
Xiaoyan Jiang
Keyword(s):  
Monte Carlo ◽  
Success Rate ◽  
Pattern Identification ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Star Pattern ◽  
Monte Carlo Experiments ◽  
Celestial Sphere ◽  
Star Pattern Identification

A pentagram star pattern identification algorithm for three-head star sensors was proposed. Its realization scheme was presented completely. Simulated star maps were produced by letting the three-head star sensor travel around the celestial sphere randomly and image the observed stars. Monte Carlo experiments were carried out. The performances of the pentagram algorithm were evaluated. It proves that its identification success rate reaches up to 98%.

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.

Precise Attitude Determination of Ship Based on Star Sensor

2013 ◽  
Vol 380-384 ◽  
pp. 995-1002 ◽  
Author(s):  
Bing Liu ◽  
Feng Chen ◽  
Tong Shuang Zhang ◽  
Dean Zhong ◽  
Lei Yang ◽  
...  
Keyword(s):  
Engineering Design ◽  
High Precision ◽  
Attitude Determination ◽  
Measured Data ◽  
Star Sensor ◽  
Single Star

This paper analyses the attitude measured model and presents the attitude determination algorithm of space TT&C ship (space tracking, telemetry, and command ship) based on single star sensor. Considering lower precision of rolling angel for single star sensor, we proposed an algorithm by integrating attitude determination and redundancy measure to obtain high precision ship attitude data. Aiming at the circumstance of space TT&C ship, the factors that influence the precision of attitude measured data such as the number of star, atmosphere refraction correct and installation elevation are analyzed, which this can provide valuable references to the engineering design for star sensor used on space TT&C ship.

Error angle determination of the star sensor with liquid cooling

2014 ◽  
Author(s):  
N. Korobova ◽  
P. Razzhivalov ◽  
V. Kalugin ◽  
S. Timoshenkov ◽  
E. Artemov
Keyword(s):  
Star Sensor ◽  
Liquid Cooling ◽  
Error Angle
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