论文部分内容阅读
最小二乘恒模算法(lscma)是阵列信号处理中广泛使用的一种能全局收敛且稳定性强的算法,但是当低信噪比的情况下它的收敛性和输出信干噪比会明显下降。本文在信号子空间特征值分解的基础上进行权值迭代,提出一种基于特征子空间的最小二乘恒模算法(eb-lscm)。经过实验仿真该算法的收敛性要强于lscma算法。
Least-squares constant modulus algorithm (lscma) is a globally convergent and robust algorithm widely used in array signal processing, but its convergence and output SINR will be obvious at low signal-to-noise ratio decline. In this paper, based on the eigenvalue decomposition of signal subspace, weights are iterated, and an eigen-subspace-based least-squares constant modulus algorithm (eb-lscm) is proposed. After experimental simulation of the convergence of the algorithm is better than lscma algorithm.