Farrell Sean M, Boominathan Vivek, Raymondi Nathaniel, Sabharwal Ashutosh, Veeraraghavan Ashok
IEEE Trans Pattern Anal Mach Intell. 2023 Aug 10;PP. doi: 10.1109/TPAMI.2023.3301553.
Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.
使用毫米波（mmWave）信号进行成像具有一个重要优势，即它们能够穿透雾、灰尘和烟雾等恶劣环境条件，而这些条件会严重降低基于光学的成像系统的性能。然而，与相机和激光雷达不同，毫米波雷达由于物理孔径小和传统信号处理技术，其角分辨率较低。另一方面，稀疏雷达成像可以在最小化功耗和读出带宽的同时增加孔径大小。本文提出了CoIR，这是一种通过合成方法进行的分析，它利用卷积解码器中的隐式神经网络偏差和压缩感知来执行高精度的稀疏雷达成像。所提出的系统与数据集无关，并且在训练或测试时不需要任何辅助传感器。我们引入了一种稀疏阵列设计，与传统的多输入多输出（MIMO）阵列设计相比，所需的天线元件数量减少了5.5倍。我们在模拟和实验毫米波雷达数据上展示了我们的系统相对于标准毫米波雷达和其他有竞争力的未训练方法所具有的改进的成像性能。
