波达方向估计中特征空间的信源数估计方法

Source number estimation using eigenspace in direction of arrival estimate

提出了特征空间法信源数估计方法,它将阵列信号的协方差估计值分别投影到信号的特征子空间和噪声的特征子空间。由于信号子空间与噪声子空间相互正交,易于由表征投影大小的判据值区分信号和噪声的贡献;本方法用的是M×M阶矩阵特征值分解,M为基元数,与波达方向估计用的相同,因此节省大量的计算量;它可以在实数空间中进行运算,进一步减少运算量。进行了数值计算,检验了判据值分布,以及在信源等功率、不等功率和空间相关色噪声等情况下特征空间法的性能。估计方法还用声纳数据进行了检验。所有这些结果均证明本估计方法性能优良。

A source number estimation using eigenspace is presented. The estimation projects covariance matrix estimate of array signal into signal eigen subspace and noise eigen subspace respectively. Due to the orthogonality between signal eigen subspace and noise eigen subspace, it is easy to differentiate the contribution of signal and noise by using the decision value, which is the magnitude of projection. Same as the direction of arrival （DOA） estimate algorithm,the estimation uses the eigenvalue decomposition of M × M order covariance matrix （M is the number of elements）.Hence a lot of computer burden can save. The estimation can be realized using decomposition in real-valued space to reduce more computer burden progressively. In computer simulation, it demonstrates the distribution of decision value, and the performance when signal＇s power is equal or different and on the condition of space correlative color noise environment. The estimaion was also tested with the sonar data .All of those results show that this estimaion has good performances.