A Hamming Distance and Spearman Correlation Based Star Identification Algorithm

Mehta, Deval; Chen, Shoushun; Low, Kay Soon

doi:10.1109/taes.2018.2845198

Cited by 22 publications

(6 citation statements)

References 26 publications

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“…K-L transform is used in [42] for rough matching, and a star walk step is performed to identify stars. [32] combines the hamming distance and Spearman correlation, increasing the identification efficiency. [20] uses image normalization and the Zernike moment to extract more invariant pattern features.…”

Section: Motivation and Related Workmentioning

confidence: 99%

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

You

Zhang

et al. 2022

Complex Intell. Syst.

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Star identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we construct a feature called the neighbor graph for each main star, transforming the star identification to the problem of finding the most similar neighbor graph. Then the rough search and graph matching are cooperated to form a dynamic search framework to solve the problem. In the rough search stage, the total edge weight in the minimum spanning tree of the neighbor graph is selected as an indicator, then the k-vector range search is applied for reducing the search scale. Spectral graph matching is utilized to achieve global matching, identifying all stars in the neighbor circle with good noise-tolerance ability. Extensive simulation experiments under the position noise, lost-star noise, and fake-star noise show that our approach achieves higher accuracy (mostly over 99%) and better robustness results compared with other baseline algorithms in most cases.

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Section: Motivation and Related Workmentioning

confidence: 99%

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

You

Zhang

et al. 2022

Complex Intell. Syst.

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“…The distance between two binary strings is the total amount of different bits at the same position [45]. We select Hamming Distance for our system based on its good performance application for recognition as well as for decoding application where similarity is used as the primary measurement for the system or method performance [46], [47], [48], [49].…”

Section: Hamming Distancementioning

confidence: 99%

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Sumari¹,

Adinandra²,

Syulistyo³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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A hostile or unfriendly aircraft will mostly fly at low-level altitude or hide behind natural obstacles to avoid Radar detection. One of the ways to detect and recognize while at the same time identifying such aircraft is to perform air observation from the ground. A technique called Visual Aircraft Recognition (VACR) has been practiced in training soldiers to recognize and find an incoming aircraft from a distance using binoculars. Remembering so many types of aircraft have their challenge. To ease the task, we have designed and developed an intelligent military aircraft recognition and identification system using the combination of Back Propagation Neural Networks (BPNN) and Information Fusion to speed up the recognition and identification. We use 13 aircraft features fused into five primary ones as the inputs to the BPNN for the recognition, while the identification uses Hamming Distance to the recognition results. With 155 data consisting of 85 military aircraft and helicopters and 70 civilian aircraft and helicopters and applying the 80:20 scheme for the training and test data, our system can obtain 95.33% and 87% accuracy at the training phase and the test phase. It also succeeds in recognizing and identifying a new military aircraft that is not in the dataset, while the Information Fusion can speed up the recognition and identification by up to 6 seconds. This impacts the acceleration of aircraft recognition and identification.

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“…Geometric approaches use geometric features such as triangles of stars to estimate the parameters [1,[10][11][12]. Pattern-based approaches use a pattern code based on the positions of neighboring stars in the image [13][14][15][16][17][18]. However, these methods are not fully blind because they use cameras specifically designed for the spacecraft.…”

Section: Related Workmentioning

confidence: 99%

Estimating camera parameters from starry night photographs

Ishikawa

Dobashi

2020

Comp. Visual Media

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We propose an efficient, specific method for estimating camera parameters from a single starry night image. Such an image consists of a collection of disks representing stars, so traditional estimation methods for common pictures do not work. Our method uses a database, a star catalog, that stores the positions of stars on the celestial sphere. Our method computes magnitudes (i.e., brightnesses) of stars in the input image and uses them to find the corresponding stars in the star catalog. Camera parameters can then be estimated by a simple geometric calculation. Our method is over ten times faster and more accurate than a previous method.

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A Hamming Distance and Spearman Correlation Based Star Identification Algorithm

Cited by 22 publications

References 26 publications

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Estimating camera parameters from starry night photographs

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