窄带信号小样本谱估计算法

Spectrum Estimation of Narrow Band Signals Using a Small Sample

摘要:

针对窄带平稳随机信号的小样本谱估计问题,结合均匀线性阵列的空间谱估计算法,提出了一种高性能小样本谱估计算法。该算法首先利用采样数据构造一个Toeplitz矩阵；然后对该矩阵进行特征值分解得到信号子空间和噪声子空间；再结合MUSIC、ESPRIT等子空间算法,即可实现对小样本采样数据的谱估计。数值仿真验证了该算法的有效性。

Abstract:

Combined with the spatial spectrum estimation algorithm of uniform linear array, a new algorithm was proposed to estimate the spectrum of narrow band stationary random signals using a small sample, which reconstructed a Toeplitz matrix using the sample data at first, and then signal subspace and noise subspace could be acquired by performing eigenvalue decomposition of the matrix. Combined with the subspace kind algorithms such as MUSIC and ESPRIT, the spectrum of signals with a small sample could be estimated. Simulation results verify that the proposed algorithm is effective.