Persymmetric Detection of Subspace Signals Embedded in Subspace Interference and Gaussian Noise

Abstract

We consider the problem of detecting subspace signals embedded in subspace interference and Gaussian noise with unknown covariance matrix. According to the criterion of generalized likelihood ratio test, we exploit persymmetry to propose an adaptive detector. Moreover, the statistical characterization of the proposed detector in the absence of target signals is obtained, which exhibits a constant false alarm rate property against the noise covariance matrix. Numerical examples illustrate that the proposed detector outperforms its counterparts, especially when the number of training data is small.