Space Target Anomaly Detection Based on Gaussian Mixture Model and Micro-Doppler Features

Wang, Jianwen; Li, Gang; Zhao, Zhichun; Ji, Jiao; Ding, Shuai; Wang, Kunpeng; Duan, Meiya

doi:10.1109/tgrs.2022.3213277

Cited by 4 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, detection methods usually use one-class classifiers to this situation [57,58]. Currently, one-class support vector machine (OCSVM) is very popular in the field of radar detection [59][60][61][62], so we choose OCSVM to determine whether a target exists.…”

Section: Classification Modulementioning

confidence: 99%

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

et al. 2023

View full text Add to dashboard Cite

In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, the resulting detection problem is called polarimetric range extended target (RET) detection, where all target scattering centers should be considered. In this paper, we propose a novel polarimetric RET detection method via adaptive range weighted feature extraction. Specifically, polarimetric features of range cells are extracted, and a pretrained attention-mechanism-based module is used to adaptively calculate range cells weights, which are used to accumulate the range cells features as detection statistics. While calculating weights, both amplitude and polarimetric features are considered. This method can make the most of polarization information and improve the accumulation effect, thus increasing the discrimination between targets and clutter. The effectiveness of the proposed method is verified compared to both popular energy-domain detection methods and existing feature-domain detection methods, and the results show that our method exhibits superior detection performance. Moreover, we further analyze our method on different target models and different clutter distributions to prove that our method is suitable for different types of targets and clutter.

show abstract