The Research on Interference Suppression Algorithms for Millimeter-Wave Radar in Multi-Interference Environments
-
摘要: 随着毫米波雷达在智能驾驶领域的广泛应用，雷达间的相互干扰问题日益凸显。干扰在时域表现为尖锐脉冲及频域本底噪声的显著升高，严重影响目标信息的获取，威胁道路交通安全。随着干扰雷达数量的增多，采用置零或插值的传统方法已无法有效抑制多干扰。为解决这一问题，该文提出了一种基于信号时域特征的联合包络修复信号重构算法，算法包括干扰区域检测与信号重构2个关键环节，首先通过干扰包络检测与包络内变换点检测的双重判据机制，提升了多干扰环境下干扰与干扰间片段有用信号检测准确性，使得干扰区域内的信号重构不仅可以利用无干扰区域有用信号也可以利用较短区域的片段有用信号。为了克服片段有用信号预测带来的信号幅度发散问题，利用希尔伯特变换对重构出的信号包络幅度协同归一化处理，使得重构出的信号更加整体连续，提升了信号重构精度。实验结果表明，当输入信干噪比(SINR)大于等于–10 dB时，输出SINR可达10 dB以上、较对比算法提升3～5 dB，且算法在实测数据中得到良好验证。Abstract:
Objective With the widespread application of millimeter-wave radar in intelligent driving, mutual interference among radars has become increasingly prominent. Interference signals appear as sharp pulses in the time domain and elevated background noise in the frequency domain, severely degrading target information acquisition and threatening road traffic safety. To address this challenge, this paper proposes a joint envelope recovery–based signal reconstruction algorithm that exploits the time-domain characteristics of signals to enhance target detection performance in multi-interference environments. Methods The proposed algorithm consists of two core steps. Step 1: Interference region detection. A dual-criterion mechanism, combining interference envelope detection with transition point detection within the envelope, is employed. This approach substantially improves the accuracy of detecting both interference regions and useful signal segments in multi-interference environments. Step 2: Signal reconstruction. The detected useful signal segments and interference-free portions are used to reconstruct the interference regions. To ensure continuity and improve reconstruction accuracy, the Hilbert transform is applied to perform normalized envelope amplitude coordination on the reconstructed signal. Results and Discussions The algorithm first detects interference regions and useful signal segments with high precision through the dual-criterion mechanism, and then reconstructs the interference regions using the detected segments. Simulation results show that the algorithm achieves an interference detection accuracy of 93.7% and a useful signal segment detection accuracy of 97.2%, exceeding comparative algorithms ( Table 3 ). The reconstructed signal effectively eliminates sharp interference pulses in the time domain, smooths the signal amplitude, and markedly improves the Signal-to-Interference-plus-Noise Ratio (SINR) in the frequency domain (Fig. 11 ). Compared with other interference suppression algorithms, the proposed method exhibits superior suppression performance (Fig. 12 ), achieving an SINR improvement of more than 3 dB in the frequency domain and maintaining better suppression effects across different SINR conditions (Fig. 13 ). In real-road tests, the algorithm successfully detects multiple interference regions and useful signal segments (Fig. 14 ) and significantly enhances the SINR after reconstruction (Fig. 15 ).Conclusions This paper proposes a joint envelope recovery–based signal reconstruction algorithm to address inaccurate target detection in multi-interference environments for millimeter-wave radar. The algorithm employs a dual-criterion mechanism to accurately detect interference regions and valid signal segments, and reconstructs the interference regions using the detected useful segments. The Hilbert transform is further applied to achieve collaborative normalization of the signal envelope. Experimental results demonstrate that the algorithm effectively identifies interference signals and reconstructs interference regions in multi-interference scenarios, significantly improving the signal-to-noise ratio, suppressing interference, and enabling accurate target information acquisition. These findings provide an effective anti-jamming solution for intelligent driving systems operating in multi-interference environments. -
表 1 实测雷达关键参数
参数 值 探测雷达 干扰雷达 载波频率${f_{\text{c}}}$(GHz) 77 77～81 信号带宽$B$(MHz) 150 825 调频上升时间(μs) 23 33 调频下降时间(μs) 3 3 脉冲重复周期${T_{\text{r}}}$(μs) 30 40 采样频率${f_{\text{s}}}$(Ms/s) 25 25 降采样因子 1 1 
下载: 导出CSV
表 2 仿真雷达关键参数
参数 值 探测雷达 干扰雷达 起始频率${f_{\text{c}}}$(GHz) 77 77 信号带宽$B$(MHz) 500 300～ 1200 脉冲重复周期${T_{\text{r}}}$($ \mathrm{\mu }\mathrm{s} $) 51.2 40 ADC采样频率(Ms/s) 40 40 目标距离(m) 40 / 干扰雷达数目 / 15
下载: 导出CSV
-
[1] 黄岩, 张慧, 兰吕鸿康, 等. 汽车毫米波雷达信号处理技术综述[J]. 雷达学报, 2023, 12(5): 923–970. doi: 10.12000/JR23119.HUANG Yan, ZHANG Hui, LAN Lyuhongkang, et al. Overview of signal processing techniques for automotive millimeter-wave radar[J]. Journal of Radars, 2023, 12(5): 923–970. doi: 10.12000/JR23119. [2] KUMBUL U, CHEN Yue, PETROV N, et al. Impacts of mutual interference analysis in FMCW automotive radar[C]. Proceedings of 2023 17th European Conference on Antennas and Propagation, Florence, Italy, 2023: 1–5. doi: 10.23919/EuCAP57121.2023.10133503. [3] WANG Yunxuan, HUANG Yan, LIU Jiang, et al. Interference mitigation for automotive FMCW radar with tensor decomposition[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(8): 9204–9223. doi: 10.1109/TITS.2024.3375658. [4] 冯翔, 刘涛, 崔文卿, 等. 基于双视角时序特征融合的毫米波雷达手势数字识别研究[J]. 电子与信息学报, 2023, 45(6): 2134–2143. doi: 10.11999/JEIT220687.FENG Xiang, LIU Tao, CUI Wenqing, et al. Handwriting number recognition based on millimeter-wave radar with dual-view feature fusion network[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2134–2143. doi: 10.11999/JEIT220687. [5] LI Zhuo, ZHANG Jun, LI Biyuan, et al. An adaptive filtering algorithm based on range-Doppler information guidance[J]. IEEE Access, 2023, 11: 145661–145678. doi: 10.1109/access.2023.3344631. [6] ZHONG Wanting, RODRÍGUEZ-PIÑEIRO J, YIN Xuefeng, et al. REDTS: RLS-enhanced Doppler target separation for automotive FMCW radar systems[C]. Proceedings of 2025 19th European Conference on Antennas and Propagation, Stockholm, Sweden, 2025: 1–5. doi: 10.23919/eucap63536.2025.10999395. [7] 赵雅琴, 宋雨晴, 吴晗, 等. 基于DenseNet和卷积注意力模块的高精度手势识别[J]. 电子与信息学报, 2024, 46(3): 967–976. doi: 10.11999/JEIT230165.ZHAO Yaqin, SONG Yuqing, WU Han, et al. High-precision gesture recognition based on DenseNet and convolutional block attention module[J]. Journal of Electronics & Information Technology, 2024, 46(3): 967–976. doi: 10.11999/jeit230165. doi: 10.11999/JEIT230165. [8] OVERDEVEST J, KOPPELAAR A G C, BEKOOIJ M J G, et al. Signal reconstruction for FMCW radar interference mitigation using deep unfolding[C]. Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes Island, Greece, 2023: 1–5. doi: 10.1109/icassp49357.2023.10096297. [9] CHOI J H, LEE H B, CHOI J W, et al. Mutual interference suppression using clipping and weighted-envelope normalization for automotive FMCW radar systems[J]. IEICE Transactions on Communications, 2016, E99. B(1): 280–287. doi: 10.1587/transcom.2015EBP3152. [10] KILLICK R, FEARNHEAD P, and ECKLEY I A. Optimal detection of changepoints with a linear computational cost[J]. Journal of the American Statistical Association, 2012, 107(500): 1590–1598. doi: 10.1080/01621459.2012.737745. [11] LIU Zhenyu, LU Wei, WU Jiayan, et al. A PELT-KCN algorithm for FMCW radar interference suppression based on signal reconstruction[J]. IEEE Access, 2020, 8: 45108–45118. doi: 10.1109/access.2020.2977098. [12] LIM S, LEE S, CHOI J H, et al. Mutual interference suppression and signal restoration in automotive FMCW radar systems[J]. IEICE Transactions on Communications, 2019, E102. B(6): 1198–1208. doi: 10.1587/transcom.2018ebp3175. [13] JUNG J, LIM S, KIM J, et al. Interference suppression and signal restoration using Kalman filter in automotive radar systems[C]. Proceedings of 2020 IEEE International Radar Conference, Washington, USA, 2020: 726–731. doi: 10.1109/RADAR42522.2020.9114723. [14] RAMEEZ M, DAHL M, and PETTERSSON M I. Autoregressive model-based signal reconstruction for automotive radar interference mitigation[J]. IEEE Sensors Journal, 2021, 21(5): 6575–6586. doi: 10.1109/JSEN.2020.3042061. [15] NEEMAT S, KRASNOV O, and YAROVOY A. An interference mitigation technique for FMCW radar using beat-frequencies interpolation in the STFT domain[J]. IEEE Transactions on Microwave Theory and Techniques, 2019, 67(3): 1207–1220. doi: 10.1109/TMTT.2018.2881154. [16] KUMUDA D K, SRIHARI P, SHESHAGIRI D, et al. A mutual interference mitigation algorithm for dense on-road automotive radars scenario[C]. Proceedings of 2023 IEEE International Conference on Electronics, Computing and Communication Technologies, Bangalore, India, 2023: 1–6. doi: 10.1109/conecct57959.2023.10234787. [17] WENG Youlong, CHEN Guangzhi, CHEN Jingxuan, et al. FRFT-based interference suppression for automotive FMCW radars[J]. IEEE Transactions on Vehicular Technology, 2025, 74(6): 8953–8965. doi: 10.1109/tvt.2025.3539790. [18] MAZHER K U, GRAFF A, GONZÁLEZ-PRELCIC N, et al. Automotive radar interference characterization: FMCW or PMCW?[C]. Proceedings of 2024 IEEE International Conference on Acoustics, Speech and Signal Processing, Seoul, Korea, Republic of, 2024: 13406–13410. doi: 10.1109/icassp48485.2024.10448296. [19] WU Yubo, HOU Y T, LI A, et al. Real-time interference mitigation for automotive radar[C]. Proceedings of 2023 IEEE Radar Conference, San Antonio, USA, 2023: 1–6. doi: 10.1109/radarconf2351548.2023.10149557. [20] WANG Yunxuan, HUANG Yan, WEN Cai, et al. Mutual interference mitigation for automotive FMCW radar with time and frequency domain decomposition[J]. IEEE Transactions on Microwave Theory and Techniques, 2023, 71(11): 5028–5044. doi: 10.1109/TMTT.2023.3275816. [21] REBUT J, OUAKNINE A, MALIK W, et al. Raw high-definition radar for multi-task learning[C]. Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 17000–17009. doi: 10.1109/cvpr52688.2022.01651. -
图(15) / 表(3)
计量
- 文章访问数: 12
- HTML全文浏览量: 9
- PDF下载量: 2
- 被引次数: 0
下载:
