论文标题
使用域Autoaptation的参数变分自动编码器从RAW ADC数据重建雷达图像重建
Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation
论文作者
论文摘要
本文介绍了一个基于参数变分的自动编码器的人类目标检测和本地化框架，直接与频率调制的持续波雷达的原始类似物到数字转换器数据一起工作。我们提出了一个具有残差和跳过连接的参数约束变异自动编码器，能够在范围内图像上生成群集和局部目标检测。此外，为了避免使用实际雷达数据在所有可能的情况下训练提出的神经网络的问题，我们提出了域的适应策略，我们首先使用基于射线跟踪的模型数据训练神经网络，然后调整网络以处理真实传感器数据。该策略可确保提出的神经网络的更好的概括和可伸缩性，即使它经过有限的雷达数据进行了训练。我们证明了我们提出的解决方案的卓越检测和定位性能，与传统的信号处理管道和以范围多普勒图像为输入相比
This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs
