Method for Improving Range Resolution of Indoor FMCW Radar Systems Using DNN

Abstract

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.

FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network

“…The CMAE serves as an index that adjusts for the MAE based on the radar’s range resolution, facilitating a comparison of the algorithm’s improvement rate. The proposed algorithm has advantages in label spacing compared to related works [ 7 , 13 , 29 ]. The experimental results demonstrate the effectiveness of the proposed algorithm within the 3–16 m range.…”

“…The experimental results demonstrate the effectiveness of the proposed algorithm within the 3–16 m range. The experiments were conducted at intervals of approximately 10 cm, offering more refined measurements compared to earlier studies [ 7 , 13 , 29 ]. Equation (14) was formulated for analyzing the performance and experimental results.…”

“…The input layer utilizes a power-normalized 256-point spectrum. The convolutional layer incorporates 8, 16, and 32 1 × 1 mask filters for each respective layer [ 13 , 14 , 15 , 16 , 17 , 18 ]. Batch normalization is performed after each convolutional layer, followed by the rectified linear unit activation function [ 19 , 20 ].…”