基于逼近噪声子空间的求根时延估计算法

Rooting time delay estimation based on noise subspace approximation

摘要:

多重信号分类(MUSIC)时延估计算法需要多径数估计，且其特征分解和谱峰搜索的计算复杂度较高。针对此问题，给出了一种基于逼近噪声子空间的求根时延估计算法。该算法利用协方差矩阵逆的高次幂逼近噪声子空间与其自身共轭转置的积，并构造多项式等式，以多项式求根的方式避免谱峰搜索，从而降低了计算复杂度。仿真结果表明，在无需多径数估计和复杂度低于MUSIC算法的条件下，所提算法的性能与MUSIC算法的性能相当，并且逼近克拉美罗界。

Abstract:

The Multiple Signal Classification(MUSIC) algorithm requires multipath number estimation. The eigenvalue decomposition and spectral peak searching feature high computational complexity. To address the issues, a new root time delay estimation based on noise subspace approximation is proposed. The proposed algorithm uses the high power inverse matrix to approach the product of both noise subspace and its conjugate transpose. The polynomial is constructed for estimating time delay. The polynomial rooting avoids the spectral peak searching and reduces the computational complexity. Simulation results show that the proposed algorithm has the similar performance as the MUSIC algorithm and approaches the Cramer-Rao Bound(CRB) without multipath number estimation; and the computational complexity of the proposed algorithm is lower than that of the MUSIC algorithm.