Source number estimation using eigenspace in direction of arrival (DOA) estimate

A source number estimation using eigenspace is presented. It projects estimated covariance matrix of array signal into signal eigen subspace and noise eigen subspace respectively. Using the orthogonality between signal eigen subspace and noise eigen subspace, it is easy to differentiate the contribution of signal and noise by using the criterion value, which is the magnitude of projection. Like the direction of arrival (DOA) estimate algorithm, the estimation uses the eigenvalue decomposition of covariance matrix with MtimesM order (M is the number of elements). Hence much computational burden can be saved. To reduce more computational burden, the estimation can be realized by the decomposition in real-valued space. Computer simulation demonstrates the distribution of criterion value and the performance on the condition of signal sources with equal power, with unequal power and space correlative color noise environment. The estimation was also tested with the sonar data. It is show that this estimation has good performances.