Angular Resolved RCS and Doppler Analysis of Human Body Parts in Motion

Abadpour, Sevda; Pauli, Mario; Schyr, Christian; Klein, Florian; Degen, Rene; Siska, Jan; Pohl, Nils; Zwick, Thomas

doi:10.1109/tmtt.2022.3218304

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Therefore, modern automotive radar sensors perceive the environment more realistically, consistently, and credibly [6][7][8]. Consequently, for evolving over-the-air (OTA) validation test methods and more consistency between the virtual validation method and reality, it is essential to prepare the HR model of radar targets [9][10][11] in the respective frequency range.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of High-resolution RCS Models of Motorcyclists in W-band Extracted from Measurements

Abadpour,

Pauli,

Siska

et al. 2024

PIER Letters

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Reliably modeling vulnerable road users (VRUs) such as motorcyclists in the virtual environment is indispensable in developing over-the-air (OTA) validation test methods. However, there are still challenges arising from many possible variations of VRUs, which may participate in the traffic scenarios. Therefore, it is essential to model them precisely and demonstrate consistency between virtual evaluation and reality. To achieve this goal, VRUs must be modeled based on their backscattering behavior which can be prepared based on high-resolution (HR) radar cross section (RCS) measurements. This work presents the backscattering behavior of motorcyclists as one of the critical VRUs in traffic scenarios. The extracted model of a motorcyclist is analyzed and compared based on HR-RCS measurements with different motorcycle variants. This evaluation is a prerequisite for developing a realistic model of VRUs and ensuring an adequate level of accuracy.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of High-resolution RCS Models of Motorcyclists in W-band Extracted from Measurements

Abadpour,

Pauli,

Siska

et al. 2024

PIER Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…With so many high-capacity deep learning models out there, this worry stands out. To further evaluate the trained model's generalizability, robustness should be considered as an additional metric [14].…”

Section: Introductionmentioning

confidence: 99%

Micro-Doppler based Human Activity Recognition using ABOA based Dual Spatial Convolution with Gated Recurrent Unit

Yarramsetti,

et al. 2024

JMC

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The through-wall capability, device-free detection of radar-based human activity recognition are drawing a lot of interest from both academics and industry. The majority of radar-based systems do not yet combine signal analysis and feature extraction in the frequency domain and the time domain. Applications like smart homes, assisted living, and monitoring rely on human identification and activity recognition (HIAR). Radar has a number of advantages over other sensing modalities, such as the ability to shield users' privacy and conduct contactless sensing. The article introduces a new human tracking system that uses radar and a classifier called Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU) to identify the subject and their behavior. The system follows the person and identifies the type of motion whenever it detects movement. One important feature is the integration of the GRU with the DSC unit, which allows the model to simultaneously capture the spatiotemporal dependence. Present prediction models just take into account spatial features that are immediately adjacent to each other, disregarding or just superimposing global spatial features when taking spatial correlation into account. A new dependency graph is created by calculating the correlation among nodes using the correlation coefficient; this graph represents the global spatial dependence, while the classic static graph represents the neighboring spatial dependence in the DSC unit. The DSC unit goes a step further by using a modified gated mechanism to quantify the various contributions of both local and global spatial correlation. While previous models performed worse, the suggested model outperformed them with an accuracy of 99.45 percent and a precision of 97.15 percent.

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An Interpretable Target-Aware Vision Transformer for Polarimetric HRRP Target Recognition with a Novel Attention Loss

Gao,

Lang,

Yeh

et al. 2024

Remote Sensing

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Polarimetric high-resolution range profile (HRRP), with its rich polarimetric and spatial information, has become increasingly important in radar automatic target recognition (RATR). This study proposes an interpretable target-aware vision Transformer (ITAViT) for polarimetric HRRP target recognition with a novel attention loss. In ITAViT, we initially fuse the polarimetric features and the amplitude of polarimetric HRRP with a polarimetric preprocessing layer (PPL) to obtain the feature map as the input of the subsequent network. The vision Transformer (ViT) is then used as the backbone to automatically extract both local and global features. Most importantly, we introduce a novel attention loss to optimize the alignment between the attention map and the HRRP span. Thus, it can improve the difference between the target and the background, and enable the model to more effectively focus on real target areas. Experiments on a simulated X-band dataset demonstrate that our proposed ITAViT outperforms comparative models under various experimental conditions. Ablation studies highlight the effectiveness of polarimetric preprocessing and attention loss. Furthermore, the visualization of the self-attention mechanism suggests that attention loss enhances the interpretability of the network.

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Angular Resolved RCS and Doppler Analysis of Human Body Parts in Motion

Cited by 3 publications

References 28 publications

Comparative Study of High-resolution RCS Models of Motorcyclists in W-band Extracted from Measurements

Comparative Study of High-resolution RCS Models of Motorcyclists in W-band Extracted from Measurements

Micro-Doppler based Human Activity Recognition using ABOA based Dual Spatial Convolution with Gated Recurrent Unit

An Interpretable Target-Aware Vision Transformer for Polarimetric HRRP Target Recognition with a Novel Attention Loss

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