Direction-of-Arrival (DOA) estimation is an important area in array signal processing. Most of DOA estimation algorithms are based on signal eigenspace theory, which utilizes the orthogonality between signal subspace and noise subspace. They require that the number of incoming signals is estimated correctly. In real application, when the estimated number of signals is not correct, the orthogonality between signal subspace and noise subspace can not be maintained any more, and the performance of DOA estination algorithm will deteriorate severely. As a result, signal number estimation is thought as a key technique in DOA estimation.

ZHU Weiqing, HU Juan, LIU Xiaodong, LIU Zhiyu, PAN Feng of Institute of Acoustics, Chinese Academy of Sciences carried out a series of studies and presented a eigenspace-based source number estimation.

It projects the 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, or the magnitude of projection. Like the Direction-of-Arrival (DOA) estimation algorithm, the estimator uses the eigenvalue decomposition of covariance matrix with order M × M, where M is the number of elements, and hence can save much computational burden. Computer simulation demonstrates the distribution of criterion value and the performance of the estimation method. The estimation method was also tested with the sonar data, which shows good performances.

This research result was published on the recently issued Chinese Journal of Acoustics(2011,30(2):115-125)