Adaptive Multi-Feature Validation Algorithm for Hovering UAV Detection via LFMCW Radar: Leveraging Micro-Doppler Spectral Characteristics

IF 1.5 4区管理学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Iet Radar Sonar and Navigation Pub Date : 2026-03-12 DOI:10.1049/rsn2.70112

Tao Chen, Jianbo Zhao, Lutao Liu

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Abstract

With the widespread use of UAVs, timely detection of non-compliant ones is critical for security. Radar excels in long-range all-weather UAV detection but faces high false alarms in cluttered environments—especially for hovering rotor UAVs, whose minimal Doppler shifts are easily masked by ground clutter. To solve this, this paper proposes the micro-Doppler spectral feature-guided LFMCW radar detection algorithm, a micro-Doppler spectral feature-guided LFMCW radar algorithm for such UAVs. Its core innovation is reconstructing signal modelling to link rotor micro-motion with radar echo spectra, plus multi-step decision logic: extracting range-Doppler (RD) maps from echoes, identifying potential targets via zero-frequency-centred peaks and verifying via three micro-Doppler features (uniform line spacing, bounded width and stable amplitude variation). Validated with a Ku-band LFMCW radar prototype, simulations show $\geq 40 %$ lower false alarms than traditional threshold methods at 30 dB SCR. Field tests in tree-cluttered environments confirm detection of micro-UAVs (RCS = $0.01 m^{2}$ ) within 2 km, with distance resoslution $\leq 5$ m and speed resolution $\leq 1$ m/s, proving the algorithm's robustness and practicality.

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基于微多普勒频谱特性的LFMCW雷达悬停无人机自适应多特征验证算法

随着无人机的广泛使用，及时发现不合规的无人机对安全至关重要。雷达在远程全天候无人机探测方面表现出色，但在混乱的环境中面临很高的假警报，特别是对于悬停旋翼无人机，其最小的多普勒频移很容易被地面杂波掩盖。针对这一问题，本文提出了微多普勒频谱特征制导LFMCW雷达检测算法，这是一种针对此类无人机的微多普勒频谱特征制导LFMCW雷达算法。其核心创新是重建信号模型，将转子微运动与雷达回波光谱联系起来，再加上多步骤决策逻辑：从回波中提取距离多普勒（RD）图，通过零频率中心峰识别潜在目标，并通过三个微多普勒特征（均匀线间距，有界宽度和稳定幅度变化）进行验证。通过ku波段LFMCW雷达样机验证，仿真结果≥40 % ${\ge} 40\%$ lower false alarms than traditional threshold methods at 30 dB SCR. Field tests in tree-cluttered environments confirm detection of micro-UAVs (RCS = 0.01 m 2 $0.01\,{\mathrm{m}}^{2}$ ) within 2 km, with distance resoslution ≤ 5 ${\le} 5$ m and speed resolution ≤ 1 ${\le} 1$ m/s, proving the algorithm's robustness and practicality.

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来源期刊

Iet Radar Sonar and Navigation 工程技术-电信学

CiteScore

4.10

自引率

11.80%

发文量

137

审稿时长

3.4 months

期刊介绍： IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation, and surveillance purposes, in aerospace and terrestrial applications. Examples include advances in waveform design, clutter and detection, electronic warfare, adaptive array and superresolution methods, tracking algorithms, synthetic aperture, and target recognition techniques.