Park Hwesoo, Kim Minji, Jung Yunho, Lee Seongjoo
Department of Information and Communication Engineering, Sejong University, Seoul 05006, Korea.
Department of Convergence Engineering of Intelligent Drone, Sejong University, Seoul 05006, Korea.
Sensors (Basel). 2022 Nov 3;22(21):8461. doi: 10.3390/s22218461.
Various studies on object detection are being conducted, and in this regard, research on frequency-modulated continuous wave (FMCW) RADAR is being actively conducted. FMCW RADAR requires high-distance resolution to accurately detect objects. However, if the distance resolution is high, a high-modulation bandwidth is required, which has a prohibitively high cost. To address this issue, we propose a two-step algorithm to detect the location of an object through DNN using many low-cost FMCW RADARs. The algorithm first infers the sector by measuring the distance to the object for each FMCW RADAR and then measures the position through the grid according to the inferred sector. This improves the distance resolution beyond the modulation bandwidth. Additionally, to detect multiple targets, we propose a Gaussian filter. Multiple targets are detected through an ordered-statistic constant false-alarm rate (OS-CFAR), and there is an 11% probability that multiple targets cannot be detected. In the lattice structure proposed in this paper, the performance of the proposed algorithm compared to those in existing works was confirmed with respect to the cost function. The difference in performance versus complexity was also confirmed when the proposed algorithm had the same complexity and the same performance, and it was confirmed that there was a performance improvement of up to five-fold compared to those in previous papers. In addition, multi-target detection was shown in this paper. Through MATLAB simulation and actual measurement on a single target, RMSEs were 0.3542 and 0.41002 m, respectively, and through MATLAB simulation and actual measurement on multiple targets, RMSEs were confirmed to be 0.548265 and 0.762542 m, respectively. Through this, it was confirmed that this algorithm works in real RADAR.
目前正在进行各种关于目标检测的研究，在这方面，对调频连续波（FMCW）雷达的研究正在积极开展。FMCW雷达需要高距离分辨率才能准确检测目标。然而，如果距离分辨率高，则需要高调制带宽，这成本过高。为了解决这个问题，我们提出了一种两步算法，通过使用许多低成本FMCW雷达的深度神经网络（DNN）来检测目标的位置。该算法首先通过测量每个FMCW雷达到目标的距离来推断扇区，然后根据推断出的扇区通过网格测量位置。这提高了超出调制带宽的距离分辨率。此外，为了检测多个目标，我们提出了一种高斯滤波器。通过有序统计恒虚警率（OS-CFAR）检测多个目标，并且存在11%的概率无法检测到多个目标。在本文提出的晶格结构中，相对于成本函数，证实了所提算法与现有工作相比的性能。当所提算法具有相同复杂度和相同性能时，也证实了性能与复杂度的差异，并且证实与先前论文相比性能提高了高达五倍。此外，本文还展示了多目标检测。通过对单个目标的MATLAB仿真和实际测量，均方根误差（RMSE）分别为0.3542和0.41002米，通过对多个目标的MATLAB仿真和实际测量，RMSE分别被证实为0.548265和0.762542米。由此证实了该算法在实际雷达中有效。
