基于子空间分解的信道阶数估计算法

提出一种新的基于子空间分解的信道阶数估计算法.首先基于子空间分解，将观测向量自相关矩阵的几何子空间按照某一正整数La分解为“信号”子空间和“噪声”子空间，由“噪声”子空间构建了一个特殊矩阵Gv，提出了Gv特征分析定理，该定理表明当且仅当La等于信道阶数时，Gv奇异并有唯一的零特征值，并进行了详细的理论和实验证明；然后根据该定理，不断修正“噪声”子空间的大小，判定Gv的奇异性，完成信道阶数的估计.仿真证明该算法不但在低信噪比条件下具有很好的估计性能，而且当信道具有较小的初始和结尾系数时，也能达到很好的效果.

Effective Channel Order Estimation Based on Subspace Decomposition

An algorithm for effective channel order estimation(NECOE) is proposed.Based on subspace decomposition,the space spanned by the eigenvectors of the autocorrelation matrix of the measurement vector can be divided into signal subspace and noise subspace,a special matrix Gv is constructed by noise subspace,and a new theorem is derived and proved,which indicates that the special matrix has only one zero-valued eigenvalue,if and only if La is equal to the effective channel order.Based on the new theorem,the chan...