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。
The Multiple Signal Classification(MUSIC) algorithm requires multipath number estimation.The eigenvalue decomposition and spectral peak searching feature high computational complexity. Toaddress 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 subspaceand its conjugate transpose. The polynomial is constructed for estimating time delay. The polynomialrooting avoids the spectral peak searching and reduces the computational complexity. Simulation resultsshow that the proposed algorithm has the similar performance as the MUSIC algorithm and approaches theCramer-Rao Bound(CRB) without multipath number estimation; and the computational complexity of theproposed algorithm is lower than that of the MUSIC algorithm.
作者
巴斌
胡捍英
郑娜娥
任修坤
BA Bin;HU Hanying;ZHENG Na’e;REN Xiukun(Institute of Navigation and Aerospace Engineering, Information Engineering University, Zhengzhou Henan 450001,China)
出处
《太赫兹科学与电子信息学报》
2016年第4期630-635,共6页
Journal of Terahertz Science and Electronic Information Technology
基金
国家高技术研究发展计划资助项目(2012AA01A502
2012AA01A505)
关键词
多重信号分类
时延估计
多项式求根
克拉美罗界
Multiple Signal Classification
time delay estimation
polynomial rooting
Cramer-Rao Bound
