Guide star selection for star pattern recognition between three FOVs

Liang, 吴量 Wu; Jian-li, 王建立 Wang; Hao-jing, 王昊京 Wang

doi:10.3788/ope.20152306.1732

Cited by 5 publications

(1 citation statement)

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“…This method can generate uniformly distributed navigation stars while ensuring that at least n stars can be measured inside a specified FOV, but the process is too complicated and the brightness of the guide stars is not considered. An improved self-organizing guide star selection method (1st-SOPM) is proposed by our team [25]. This method is designed for the three-FOV star sensor in the visible waveband and the number of the selected stars is inversely proportional to the size of FOV, so when a narrow FOV is used for high navigation accuracy in the daytime application, the guide star catalog generated by 1st-SOPM is too large.…”

Section: Introductionmentioning

confidence: 99%

Guide Star Selection for the Three-FOV Daytime Star Sensor

Wang

et al. 2019

Sensors

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To realize the application of the star sensor in the all-day carrier platform, a three-field-of-view (three-FOV) star sensor in short-wave infrared (SWIR) band is considered. This new prototype employs new techniques that can improve the detection capability of the star sensor, when the huge size of star identification feature database becomes a big obstacle. Hence, a way to thin the guide star catalog for three-FOV daytime star sensor is studied. Firstly, an introduction of three-FOV star sensor and an example of three-FOV daytime star sensor with narrow FOV are presented. According to this model and the requirement of triangular star identification method, two constraints based on the number and the brightness of the stars in FOV are put forward for guide star selection. Then on the basis of these constraints, the improved spherical spiral method (ISSM) is proposed and the optimal number of reference points of ISSM is discussed. Finally, to demonstrate the performance of the ISSM, guide star catalogs are generated by ISSM, magnitude filter method (MFM), 1st order self-organizing guide star selection method (1st-SOPM) and the spherical spiral method (SSM), respectively. The results show that the guide star catalog generated by ISSM has the smallest size and the number and brightness characteristics of its guide stars are better than the other methods. ISSM is effective for the guide star selection in the three-FOV daytime star sensor.

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Section: Introductionmentioning

confidence: 99%

Guide Star Selection for the Three-FOV Daytime Star Sensor

Wang

et al. 2019

Sensors

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SIMU/Triple star sensors integrated navigation method of HALE UAV based on atmospheric refraction correction

Gao

Wang

Xu³

2022

J. Navigation

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To achieve autonomous all-day flight by high-altitude long-endurance unmanned aerial vehicle (HALE UAV), a new navigation method with deep integration of strapdown inertial measurement unit (SIMU) and triple star sensors based on atmospheric refraction correction is proposed. By analysing the atmospheric refraction model, the stellar azimuth coordinate system is introduced and the coupling relationship between attitude and position is established. Based on the geometric relationship whereby all the stellar azimuth planes intersect on the common zenith direction, the sole celestial navigation system (CNS) method by stellar refraction with triple narrow fields of view (FOVs) is studied and a loss function is built to evaluate the navigation accuracy. Finally, the new SIMU/triple star sensors deep integrated navigation method with refraction correction upgraded from the traditional inertial navigation system (INS)/CNS integrated method can be established. The results of simulations show that the proposed method can effectively restrain navigation error of a HALE UAV in 24 h steady-state cruising in the stratosphere.

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Multi-Stage Star Image Identification Method of Three Field-of-View Star Sensor

Gou

Chen

et al. 2019

西北工业大学学报

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To improve the low efficiency and low navigation star identification rate of existing star image identification methods for three field-of-view (FOV) star sensor, a multi-stage star image identification method is proposed. Firstly, the generalized regression neural network which has only one adjustable parameter, is used to identify the star images in each field-of-view. Secondly, the star angular distance saved in the navigation star database is used to verify the identification results, and then the optical directions of the three FOVs are calculated by using the correctly identified navigation stars. Thirdly, the optical directions are utilized to auxiliary correct the unidentified and erroneous identified navigation stars. Finally, the high-accuracy probe attitude is estimated by using the correctly identified navigation stars in the three FOVs. The simulation results show that the identification rates of the experimental samples is of 98.9% when the standard deviation of star centroid positioning error increases to 0.07 pixels, but the identification time is only of 8.464 5 ms. Meanwhile, since the three field-of-view star sensor captures the more dispersed navigation stars, the probe attitude accuracy of yaw, pitch and roll angles by using the present method is improved evidently, which is of 1.205 8″, 1.086 7″, and 1.201 8″, respectively.

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Guide star selection for star pattern recognition between three FOVs

Cited by 5 publications

References 0 publications

Guide Star Selection for the Three-FOV Daytime Star Sensor

Guide Star Selection for the Three-FOV Daytime Star Sensor

SIMU/Triple star sensors integrated navigation method of HALE UAV based on atmospheric refraction correction

Multi-Stage Star Image Identification Method of Three Field-of-View Star Sensor

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