Object classification on noise-reduced and augmented micro-doppler radar spectrograms

Erdogan, Alperen; Güney, Selda

doi:10.1007/s00521-022-07776-3

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…The non-contact monitoring approach has the potential to revolutionize healthcare and security applications. Recently, to mitigate the expensive and time-consuming process of obtaining MD signatures, augmentation [28] has been applied to raw MD signatures, where a learning-based approach [29] is also designed using the generative adversarial network [30]. Especially, MD signatures on human gait characteristics [4] are used for radar human identification (RHI), where the system processes the information of movements from fine-grained human body parts in an uncontrolled scenario where a target is allowed to walk around in a free and spontaneous way.…”

Section: B Deep Learning For Micro-doppler Signaturesmentioning

confidence: 99%

Causal Localization Network for Radar Human Localization With Micro-Doppler Signature

Yoon,

Koo,

Shim

et al. 2024

IEEE Access

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Micro-Doppler (MD) signature includes unique characteristics by different-sized body parts such as arms, legs, and torso. Existing radar identification systems have made an effort to classify the identification of humans using these characteristics presented in MD signatures while achieving a remarkable performance of classification. However, we argue that the radar identification system also should be extended to perform more fine-grained tasks to achieve the flexibility of the identification. In this paper, we introduce a radar human localization (RHL) task, which involves temporally localizing human identifications within untrimmed MD signatures. To enable RHL, we have constructed a micro-Doppler dataset referred to as IDRad-TBA. Furthermore, we propose Causal Localization Network (CLNet) as the RHL baseline system built upon the IDRad-TBA dataset. CLNet employs a novel temporal causal prediction approach for MD signature localization. Experimental results validate the effectiveness of CLNet in performing the RHL task. Our project is available at: https://github.com/dbstjswo505/CLNet INDEX TERMS deep learning, temporal human identification, micro-Doppler radar, information retrieval.

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Section: B Deep Learning For Micro-doppler Signaturesmentioning

confidence: 99%