Passive Radar Low Slow Small Detection Dataset (LSS-PR-1.0) and Multi-domain Feature Extraction and Analysis Methods
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摘要: 被动雷达在预警探测和低慢小目标(LSS)检测中具有重要作用。由于被动雷达信号辐射源不可控,目标特性更为复杂,导致检测和识别极其困难。该文构建了被动雷达低慢小探测数据集(LSS-PR-1.0),该数据集包含了直升机、无人机、快艇、客轮4种典型海空目标的雷达回波信号,以及低高海况的海杂波数据,为该领域研究提供了数据支撑。在目标特征提取和分析方面,首先采用奇异值分解海杂波抑制方法,去除海杂波强Bragg峰对目标回波的影响。在此基础上,提出4类10种多域特征提取和分析方法,包括时域特征(相对平均幅度)、频域特征(频谱特征、多普勒瀑布图、距离多普勒特征)、时频域特征、运动特征(航向差、航迹参数、速度变化区间、速度变异系数、加速度)等。基于实测数据对4种海空目标特性进行了对比分析,总结各类目标特性规律,为后续目标识别奠定了基础。Abstract: Passive radar plays an important role in early warning detection and Low Slow Small (LSS) target detection. Due to the uncontrollable source of passive radar signal radiations, target characteristics are more complex, which makes target detection and identification extremely difficult. In this paper, a passive radar LSS detection dataset (LSS-PR-1.0) is constructed, which contains the radar echo signals of four typical sea and air targets, namely helicopters, unmanned aerial vehicles, speedboats, and passenger ships, as well as sea clutter data at low and high sea states. It provides data support for radar research. In terms of target feature extraction and analysis, the singular-value-decomposition sea-clutter-suppression method is first adopted to remove the influence of the strong Bragg peak of sea clutter on target echo. On this basis, four categories of ten multi-domain feature extraction and analysis methods are proposed, including time-domain features (relative average amplitude), frequency-domain features (spectral features, Doppler waterfall plot, and range Doppler features), time-frequency-domain features, and motion features (heading difference, trajectory parameters, speed variation interval, speed variation coefficient, and acceleration). Based on the actual measurement data, a comparative analysis is conducted on the characteristics of four types of sea and air targets, summarizing the patterns of various target characteristics and laying the foundation for subsequent target recognition.
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表 1 被动雷达基本参数
Table 1. Basic parameters of passive radar
参数 数值 可接收频率范围 470~806 MHz DTMB信号带宽 7.56 MHz 距离分辨单元 39.68 m 方位精度 1° 数据更新周期 1 s 表 2 文中使用的外辐射源雷达数据
Table 2. Passive radar data used in the paper
目标类型 RD数据文件名 编号 快艇 20230616162327 _PR_RD_快艇02#S2 20240525180546 _PR_RD_快艇12#S12 客轮 20230617153740 _PR_RD_客轮01#L1 20231125153335 _PR_RD_客轮07#L7 20240522153628 _PR_RD_客轮11#L11 直升机 20230705162446 _PR_RD_直升机01#H1 20231114193839 _PR_RD_直升机05#H5 20240515174613 _PR_RD_直升机09#H9 无人机 20240113131753 _PR_RD_无人机02#D2 20240407094458 _PR_RD_无人机09#D9 表 3 海空典型目标时域特征比较
Table 3. Comparison of time-domain characteristics of typical targets in sea and air
目标类型 相对平均幅度(RAA) 直升机 0.34~2.67 无人机 0.39~2.61 快艇 0.20~3.93 客轮 0.31~3.52 表 4 目标双基距离和双基速度
Table 4. Bistatic range and velocity of targets
目标类型 双基距离(m) 双基速度(m/s) 直升机 8730 78 无人机 1231 22 快艇 5955 31 客轮 9647 –9 表 5 海空典型目标频域特征值比较
Table 5. Comparison of characteristic values of typical targets in the frequency-domain of sea and air
目标类型 频谱峰值与均值之比(FPAR) 直升机 2.63~82.96 无人机 3.66~37.13 快艇 3.00~132.71 客轮 2.91~116.79 表 6 海空典型目标的速度特征
Table 6. Speed characteristics of a typical target in the sea and air
目标类型 速度变异系数 归一化加速度过零点数 直升机 0.21~0.45 0.15~0.37 无人机 0.13~0.19 0.25~0.45 快艇 0.10~0.30 0.10~0.40 客轮 0.28~0.72 0.03~0.32 表 7 海空典型目标航迹特征
Table 7. Typical target track characteristics in the sea and air
目标类型 航向差 航迹参数 直升机 2.95~13.97 0.65~0.94 无人机 7.25~13.30 0.80~0.97 快艇 6.09~13.87 0.70~0.97 客轮 9.75~14.37 0.26~0.65 表 8 被动雷达海空典型目标特征汇总
Table 8. Summary of typical target characteristics in the sea and air for passive radar
特征域 特征 结论 时域 时域回波图 直升机、客轮目标区别较大,快艇和无人机较相似 频域 多普勒瀑布图 帧间特征,表示目标的全局的特性,各目标区分度较大 距离多普勒图 直升机、无人机作为空中目标与海面有一定的高度,受海面影响小,距离扩展不明显或小,
而客轮和快艇目标受海面影响大,距离扩展明显多普勒谱图 直升机的微多普勒明显,无人机的微多普勒不明显,快艇的尖峰比客轮尖峰短且杂波影响比客轮更大 时频 时频图 直升机目标为正弦曲线,无人机目标与零频呈对称分布,能量较强的部分在正负频率交错呈现,
快艇略有曲折、杂波多,客轮为直线、杂波少运动 速度变异系数 对各目标有一定的区分能力,但有重叠部分 -
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