【摘要】 基于自回归(auto-regressive,AR)模型的参数化虚拟孔径合成算法无法应用于灵活的稀疏阵列构型,在单快拍和低信噪比(signal-to-noise ratio,SNR)时对极点个数和位置估计不准。该文提出了基于迭代自适应(iterative adap-tive approach,IAA)的非参数化虚拟孔径合成方法,该方法无需估计极点信息,通过迭代计算虚拟孔径的空间谱并恢复虚拟阵元数据。分析和仿真结果表明:该方法在单快拍和低信噪比条件下比参数化方法具有更好的分辨性能和稳健性,可用于阵元位置随机空缺的情况。更多还原

【Abstract】 In virtual aperture synthesis,the auto-regressive(AR) model-based parametric methods cannot accurately estimate the number and positions of poles in low signal-to-noise ratio(SNR) environments with a single snapshot of data.They are also not applicable to flexible sparse arrays.A non-parametric algorithm is presented for virtual aperture synthesis based on the iterative adaptive approach(IAA).This method iteratively calculates the spatial spectrum of the virtual aperture to recover the virtual elements’ data.Analyses and simulations show that the method has better resolution and robustness than parametric methods with low SNR signals with single snapshot data and can be applied to a random vacant array configurations.更多还原