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Multiband FMCW Radar LSS-target Detection Dataset (LSS-FMCWR-1.0) and High-resolution Micromotion Feature Extraction Method
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摘要: 无人机等低慢小目标探测对雷达目标检测和识别技术提出了很高的挑战，迫切需要构建相关数据集，支撑低慢小探测技术的发展和应用。该文公开了一个多波段调频连续波(FMCW)雷达低慢小目标探测数据集，基于Ku波段和L波段的FMCW雷达采集6种类型的无人机回波数据，通过雷达调制周期和调制带宽，具备不同时域和频域分辨和测量能力，构建了多波段FMCW雷达低慢小探测数据集(LSS-FMCWR-1.0)。为了进一步提升无人机微动特征提取能力，该文提出基于局部极大值同步提取变换的无人机微动提取和参数估计方法，在短时傅里叶变换的基础上提取时频能量最大值，保留有用信号分量，实现精细化时频表示。基于LSS-FMCWR-1.0进行验证分析，结果表明该方法相较于传统时频方法，熵值平均降低了5.3 dB，旋翼叶长估计误差降低了27.7%，所提方法兼顾高时频分辨率和较高的参数估计精度，为后续目标识别奠定了基础。
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关键词:
- 低慢小目标 /
- 调频连续波雷达 /
- 微动特性 /
- 局部极大值同步提取变换 /
- 公开数据集
Abstract: Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities. -
图 3 三角波调制FMCW雷达发射和接收信号示意图
Figure 3. Diagram of the transmitted and received signals of the triangular wave modulated FMCW radar
图 5 各类型无人机时频谱图及距离周期图
Figure 5. Time spectrum and range Doppler of various types of unmanned aerial vehicles
图 6 固定翼无人机的时频图(06-2023.5.8-0.3-100-9-K (1))
Figure 6. Time spectrum diagram of fixed wing UAV
图 11 去抖动干扰前和去抖动干扰后的距离周期图(LSS-FMCWR-1.0:06-2023.5.8-0.3-100-9-K (1).mat)
Figure 11. Data before and after removing jitter interference (LSS-FMCWR-1.0:06-2023.5.8-0.3-100-9-K (1).mat)
图 12 不同时频方法的Renyi熵值
Figure 12. Renyi entropy values for different time-frequency methods
图 13 在不同信噪比下，不同时频方法的Renyi熵值
Figure 13. Under different SNRs, the Renyi entropies of the different time-frequency analysis methods
图 14 悟2无人机信号STFT和LSET (LSS-FMCWR-1.0: 04-2023.5.8-0.3-100-13-L (18).mat)
Figure 14. STFT and LSET of Wu 2 UAV signal (LSS-FMCWR-1.0: 04-2023.5.8-0.3-100-13-L (18).mat)
图 17 LSS-FMCWR-1.0：多波段FMCW雷达低慢小探测数据集
Figure 17. Release webpage of LSS-FMCWR-1.0: Multiband FMCW radar LSS-target echo dataset
表 1 Ku波段和L波段FMCW雷达主要技术参数
Table 1. Ku-band and L-band FMCW radar main technical indicators
FMCW雷达
波段工作频率(GHz) 调制带宽(MHz) 调制周期
(ms)采样频率(kHz) Ku波段 23.7 10～2000 0.200～10.000 500 L波段 1.4～1.5 100 0.300～8.192 500 
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表 2 无人机主要技术参数
Table 2. The main technical parameters of the UAV
无人机类型 旋翼个数 转速(r/min) 叶片长度(cm) 大疆御2 4 1950 11 大疆精灵 4 1320 13 大疆M350 4 1750 11 大疆悟2 4 1500 19 大疆M600 6 1620 12 固定翼无人机 1 360 12
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表 3 无人机采集参数设置(目标距离为估值)
Table 3. UAV acquisition parameter settings
无人机类型及编号 调制带宽(MHz) 调制周期(ms) 目标距离(m) 雷达波段
Ku/L大疆御2(01) 100 0.300 9 Ku+L 1.024 9 Ku+L 大疆精灵(02) 100 0.300 9 Ku+L 1.024 9 Ku+L 大疆M350(03) 100 0.300 12 Ku+L 1.024 12 Ku+L 200 0.300 12 Ku 1.024 12 Ku 300 0.300 12 Ku 1.024 12 Ku 大疆悟2(04) 100 0.300 11 Ku+L 1.024 11 Ku+L 大疆M600(05) 100 0.300 8 Ku 11 L 1.024 11 Ku 13 L 固定翼无人机(06) 100(正视) 0.300(低速) 9 Ku+L 0.300(高速) 9 Ku+L 4.192 9 Ku+L 500 4.192 9 Ku 100(侧视) 0.300(低速) 9 Ku+L 0.300(高速) 9 Ku+L 4.192 9 Ku+L 500 4.192 9 Ku
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表 4 时频方法的运算时间
Table 4. Time-frequency method execution time
时频方法 运行时间(s) STFT 0.019 SST 1.053 SET 0.063 LSET 0.503
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表 5 悟2无人机叶片转速和长度的估计
Table 5. Estimation of blade speed and length for Wu2 UAV
时频分析方法 转速(r/s) 叶片长度(cm) 理论值 估计值 相对误差 理论值 估计值 相对误差 STFT 25 22.83 8% 19 25.6 34.7% LSET 25 23.4 6% 19 20.5 7.0%
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