一种基于短采样数据的快速超分辨SSMUSIC算法

A New Low-Complexity Super-Resolution SSMUSIC Algorithm Based on Short Data Lengths

基于多级维纳滤波(MSWF)理论,针对跳频通信或突发短时通信等情形下所造成的短采样数据,提出了利用CSA MSWF和Lanczos Algorithm快速实现在有限数据长度相较经典MUSIC算法角度估计性能更优、角度分辨率更高、算法鲁棒性更好的SSMUSIC算法。本文提出的MSWF SSMUSIC方法,不仅保持了SSMUSIC算法在短采样、低信噪比情形下角度估计性能更优、角度分辨率更高、算法鲁棒性更好等优点,而且避免了SSMUSIC算法为求得信号子空间、噪声子空间、协方差矩阵信号部分所对应的大特征值及噪声功率的估计而必须对高维协方差矩阵进行特征分解所带来的大的计算量的问题,扫除了SSMUSIC算法在短数据情形下由理论走向实际工程应用的主要障碍。仿真实验证明了该方法的有效性和可行性。

In this paper,we propose a new fast super-resolution SSMUSIC（Signal Subspace Scaled MUSIC） algorithm for short data lengths based on CSA MSWF（Correlation Subtraction Algorithm Multi-Stage Wiener Filter） theory.The proposed MSWF SSMUSIC method has better angular performance and lower computational cost than MUSIC,particularly in the low signal-to-noise ratio（SNR） and small measurement support regimes.Also,it has lower computational burden than SSMUSIC algorithm with slight performance loss.The proposed approach is quite suitable for short data lengths or when the environment is quite dynamic,with improved performance and reduced computational load.Numerical results are presented to illustrate the effectiveness and validity of the proposed methodology.