多重观测矢量模型下的微动目标特征提取

针对传统基于压缩感知(CS)理论的微动目标特征提取方法不适用于宽带雷达目标的情况，以线性调频信号体制雷达为例，通过分析微动目标回波的内在特性，构建了一种微动目标回波的多重观测矢量(MMV)模型。结合频率估计算法与正交匹配追踪(OMP)算法进行MMV模型的稀疏表达求解，从而获得微动目标的特征参数。仿真结果表明，与传统基于单重观测矢量(SMV)模型的微动特征提取方法相比，噪声环境下采用MMV模型进行微动特征提取具有更强的鲁棒性。

Micro-Doppler target feature extraction with a Multiple Measurement Vector model

As the traditional Compressive Sensing(CS) based Micro-Doppler(M-D) feature extraction methods cannot be utilized in wideband radar, the internal property of returned signals induced by the M-D targets is focused, and a modified Multiple Measurement Vectors(MMV) model is constructed based on the Linear Frequency Modulated(LFM) signal. Combining the frequency estimation approaches and the Orthogonal Matching Pursuit(OMP) algorithm, the sparse solution of the MMV model is solved, and therefore the M-D features are obtained. Simulation results indicate that, the proposed approach outperforms traditional Single Measurement Vector(SMV) based methods in the robustness, especially under noisy conditions.