Enhanced Resolution for Closely-Spaced Objects From Optical Sensor Using FPA Offset

Tian, Zijiao; Finelli, Andrew; Milgrom, Benny; Yang, Kaipei; Bar‐Shalom, Yaakov; Willett, Peter

doi:10.1109/taes.2022.3221062

IEEE Trans. Aerosp. Electron. Syst.

2023

DOI: 10.1109/taes.2022.3221062

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Enhanced Resolution for Closely-Spaced Objects From Optical Sensor Using FPA Offset

Zijiao Tian

Andrew Finelli

Benny Milgrom

et al.

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2024

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“…In addition, Lu et al used the generalized likelihood ratio test (GLRT) based on a linearized observation model for the measurement extraction of two targets with unknown equal intensities in the focal plane [17]. Tian et al used two frames of FPA in which the targets were slightly shifted to estimate the positions and intensities of two closely-spaced objects with unknown intensities and used GLRT for performing decision-making [18].…”

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confidence: 99%

A Resolution and Localization Algorithm for Closely-Spaced Objects Based on Improved YOLOv5 Joint Fuzzy C-Means Clustering

Li,

Chen,

Rao

et al. 2024

IEEE Photonics J.

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The presence of numerous space objects poses significant challenges to spacecraft launch, operation, and space security. To address this issue, a novel algorithm based on improved YOLOv5 joint Fuzzy C-Means (FCM) has been proposed for discerning and localizing closely-spaced objects (CSOs) in space. The algorithm employs the lightweight neural network YOLOv5 to estimate the quantity of the targets and employs the improved FCM localizing. In the first stage, it enhances the precision of CSOs quantity estimation by integrating small target layers and the Convolutional Block Attention Module (CBAM) into the YOLOv5 network. In the second stage, leveraging the estimated target quantity as input, the FCM algorithm based on Lanczos3 interpolation and particle distribution was used for the sub-pixel localization of each target cluster. The experimental results show that the algorithm's quantity estimation accuracy is more than 90%, and the average localization error is within 0.32 pixels. Moreover, the average running time of the algorithm is less than 0.034 s. Compared with other methods, the algorithm shows excellent performance, and its effectiveness is important for target tracking, 3D localization, and detailed analysis.

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confidence: 99%

A Resolution and Localization Algorithm for Closely-Spaced Objects Based on Improved YOLOv5 Joint Fuzzy C-Means Clustering

Li,

Chen,

Rao

et al. 2024

IEEE Photonics J.

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Enhanced Resolution for Closely-Spaced Objects From Optical Sensor Using FPA Offset

Cited by 1 publication

References 24 publications

A Resolution and Localization Algorithm for Closely-Spaced Objects Based on Improved YOLOv5 Joint Fuzzy C-Means Clustering

A Resolution and Localization Algorithm for Closely-Spaced Objects Based on Improved YOLOv5 Joint Fuzzy C-Means Clustering

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