Kang Sung-Wook, Jang Min-Ho, Lee Seongwook
School of Electronics and Information Engineering, College of Engineering, Korea Aerospace University, Goyang 10540, Gyeonggi-do, Korea.
Sensors (Basel). 2022 Jul 25;22(15):5552. doi: 10.3390/s22155552.
In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.
一般来说，恒虚警率算法（CFAR）在汽车调频连续波（FMCW）雷达系统中被广泛用于自动检测目标。然而，在传统的CFAR算法中，如果保护单元的数量、训练单元的数量以及虚警概率设置不当，目标检测性能会严重下降。因此，我们提出一种方法，在多输入多输出FMCW雷达系统中使用基于卷积神经网络的自动编码器（AE）来取代CFAR算法。在自动编码器中，整个检测结果在编码器端被压缩，只有重要的信号分量在解码器端被恢复。在这项工作中，通过改变隐藏层的数量和每层中的滤波器数量，确定了在目标检测结果中显示出高信噪比的自动编码器结构。为了评估所提方法的性能，将基于自动编码器的目标检测结果与传统CFAR算法的目标检测结果进行比较。通过与标记有实际目标位置的数据计算相关系数，所提基于自动编码器的目标检测显示出最高的相似性，相关性为0.73或更高。
