基于改进Kalmus滤波的悬停无人机检测技术

针对复杂杂波环境下悬停无人机检测问题,提出了一种改进的Kalmus滤波 剩余回波时域均值相消 自适应CFAR联合处理算法,对无人机微多普勒检测,实现空管监视目的。通过改进的Kalmus滤波器进行频域滤波,同时对目标回波高频信号和零频信号抑制,并提高零频附近微多普勒信号增益。采用剩余回波均值相消进行二次滤波,提高无人机高速旋翼的多普勒特征信号信噪比,采用短时傅里叶算法检测目标区域多普勒变化,最后通过恒虚警处理,进一步抑制杂波,提取微多普勒信息。试验结果表明本文算法可以对悬停无人机的旋翼多普勒特征进行有效检测,目标多普勒信号幅值提升了约20 dB,实现低空监视管控目的。

Hovering UAV detection technology based on improved Kalmus filtering

Aiming at the detection problem of hovering UAV in complex clutter environment, an improved Kalmus filter residual echo time domain mean cancelation adaptive CFAR joint processing algorithm is proposed to detect micro Doppler of UAV and realize the purpose of air traffic control monitoring. The improved Kalmus filter is used for frequency domain filtering, and the high frequency signal and zero frequency signal of target echo are suppressed at the same time, and the micro Doppler signal gain near zero frequency is improved. The residual echo mean cancellation was used for secondary filtering to improve the signal to noise ratio of Doppler characteristic signals of the UAV high speed rotor. The short time Fourier algorithm was used to detect Doppler changes in the target region. Finally, the constant false alarm processing was used to further suppress clutter and extract micro Doppler information. The experimental results show that the proposed algorithm can effectively detect the rotor Doppler characteristics of hovering UAV, and the amplitude of the target Doppler signal is increased by about 20 dB to achieve the purpose of low altitude monitoring and control.