Weak Target Detection Based on Message Passing Network Fusion with Deep Neural Network

Xu, Cong; Liu, Haicheng; Miao, Xinglei

doi:10.1145/3503047.3503101

Proceedings of the 3rd International Conference on Advanced Information Science and System 2021

DOI: 10.1145/3503047.3503101

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Weak Target Detection Based on Message Passing Network Fusion with Deep Neural Network

Cong Xu¹,

Haicheng Liu²,

Xinglei Miao

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2024

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Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

Geng,

Zheng,

Zhang

et al. 2024

Trans Emerging Tel Tech

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Moving target detection in a heavy clutter area is a challenging problem in the field of radar automatic target detection. Within the framework of track posture hypothesis testing, the radar‐blips‐oriented target detection algorithms named ErgSad and RanSaD are proposed for single target and formation targets detection, respectively in this paper. With the assumption that target motion is a linear Gaussian process in a short time, the algorithms use dual blips in different scans to generate the track hypothesis and test the hypothesis with residual blips in other scans to determine the existence of moving targets. To reduce time consumption and minimize the error of model estimation, the sampling rules and the supporting domain related to timestamps of track hypothesis models were designed. Simulation data experiments show that the proposed algorithms have more superior performance to detect targets than the state‐of‐the‐art algorithms in the serious clutter environment.

show abstract

Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

Geng,

Zheng,

Zhang

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

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Weak Target Detection Based on Message Passing Network Fusion with Deep Neural Network

Cited by 1 publication

References 19 publications

Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

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