A star pattern recognition algorithm for cameras with large FOV

Duan, Yu; Li, Minghui; Niu, Zhaodong; Jing, Peiliang; Chen, Zengping

doi:10.1080/09500340.2017.1377304

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…In the integrated navigation system, the reference star map is obtained using the above steps, and the ‘hypothesis test’ can be used to efficiently extract and identify the star points by matching the measured star map with the reference star map (Duan et al., 2018). When the reference star map is known, the star points can be found in a certain neighbourhood of the image coordinates of the navigation star according to the reference star map, as shown in Figure 4.…”

Section: All-sky Star Identification Algorithmmentioning

confidence: 99%

Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Yang²,

Xiao

et al. 2022

J. Navigation

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The fish-eye star sensor with a field of view (FOV) of 180° is an important piece of equipment for attitude determination, which improves the visibility of stars significantly. However, it also brings the star identification (star-ID) difficulties because of imprecise calibrations. Thus, a fish-eye star-ID algorithm supported by the integration of the precise point positioning/inertial navigation system (PPP/INS) is proposed. At first, a reference star map is generated in combination with the distortion model of the fish-eye camera based on the position and attitude information from the PPP/INS. Then the star points are extracted in a specific neighbourhood of the reference star points. Subsequently, the extracted star points are individually tested and identified according to angular distance error. Finally, the real-time precise attitude is determined based on the star-ID results. Experimental results show that, 270–310 stars can be identified in a fish-eye star map with an average time of 0.03 s if the initial attitude error is smaller than 1.5° and an attitude determination accuracy better than 10″ can be achieved by support from PPP/INS.

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Section: All-sky Star Identification Algorithmmentioning

confidence: 99%

Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Yang²,

Xiao

et al. 2022

J. Navigation

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Celestial Navigation and Positioning Method Based on Super-Large Field of View Star Sensors

Zhang,

Tong

et al. 2023

Lecture Notes in Electrical Engineering

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Implementation of Real-time Detection Algorithm for Space Debris Based on Multi-core DSP

Sun

Niu

Yao

2019

J. Phys.: Conf. Ser.

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The purpose of this paper is to implement a real-time detection algorithm for CCD star image on multi-core DSP platform. It can effectively identify weak stellar targets and space debris. It mainly includes two parts: the space debris detection method and DSPs’ project. there are several steps in this algorithm: background suppression, smear removing, stellar targets detection, space debris detection, track establishment. As for implementation, This project includes three parts: communication port design, pipeline design and memory partition. The final results show that the platform given in this paper can successfully detect weak stellar targets and space debris in real time, and establish tracks with high-accuracy of the space debris in complex environment. The maximum time of processing a 2048*2048 (in gray 16-bit sets) image is approximately 600ms.

show abstract

A star pattern recognition algorithm for cameras with large FOV

Cited by 4 publications

References 9 publications

Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Celestial Navigation and Positioning Method Based on Super-Large Field of View Star Sensors

Implementation of Real-time Detection Algorithm for Space Debris Based on Multi-core DSP

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