低脉冲重复频率条件下无人机微动参数提取

Micro-motion parameters extraction for UAV under LPRF condition

针对低脉冲重复频率条件下的无人机微动特征提取问题,提出一种基于原子放缩正交匹配追踪(orthogonal matching pursuit,OMP)分解的微动参数估计方法。首先,通过计算时频谱熵从无人机回波数据中筛选出具有显著微动特征的信号。其次,采用变分模态分解(variational modal decomposition,VMD)算法提取旋翼叶片的旋转频率,提出了基于原子放缩的OMP分解方法实现了对无人机旋翼叶片长度的估计。仿真实验表明所提方法相比于传统的OMP方法和VMD-OMP方法都具有明显优势。最后,采用实测数据验证了所提算法的有效性。

For solving the micro-motion parameters extraction problem of unmanned aerial vehicle(UAV)under low pulse repetition frequency(PRF)condition,a method of micro-motion parameters estimation based on atomic scaling orthogonal matching pursuit(OMP)decomposition is proposed.Firstly,the spectrum entropy of time-frequency distribution is calculated to sift out the proper segment signals from the echoes with significant micro-motion features.Then,the rotational frequency is obtained by using the variational modal decomposition(VMD),and an atomic scaling OMP algorithm is proposed to estimate the radius of the UAV rotor blade.Simulation results show that the proposed method has obvious advantages over the traditional OMP decomposition method and VMD-OMP method.Finally,the effectiveness of the proposed algorithm is also verified by using the actual measurement data.