首页|多波段FMCW雷达低慢小探测数据集(LSS-FMCWR-1.0)及高分辨微动特征提取方法

多波段FMCW雷达低慢小探测数据集(LSS-FMCWR-1.0)及高分辨微动特征提取方法

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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.
Multiband FMCW Radar LSS-target Detection Dataset(LSS-FMCWR-1.0)and High-resolution Micromotion Feature Extraction Method
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.

Low Slow Small(LSS)tagertFrequency Modulate Continuous Wave(FMCW)radarMicromotion characteristicsLocal maximum SynchroExtracting Transform(LSET)Public dataset

陈小龙、袁旺、杜晓林、于刚、何肖阳、关键、汪兴海

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海军航空大学 烟台 264001

烟台大学 烟台 264005

济南大学 济南 250022

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

国家自然科学基金国家自然科学基金山东省自然科学基金

6222212061931021ZR201YQ43

2024

10.12000/JR23142

雷达学报
中国科学院电子学研究所 中国雷达行业协会

雷达学报

CSTPCD北大核心EI
影响因子：0.667
ISSN：2095-283X
年,卷(期)：2024.13(3)
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