166 / 2019-12-15 14:13:00

Adaptive Subspace Detection Exploiting Persymmetry in Homogeneous Environments

Adaptive detection; persymmetry; mismatch; subspace model

A target detection problem in homogeneous Gaussian noise with unknown covariance matrix is examined using multiple observations which may be collected from multiple range cells, bands and/or coherent processing intervals. To take into consideration the mismatch in the target steering vector, we adopt a subspace model where the target steering vector is assumed to lie in a subspace spanned by the column vectors of a known matrix with an unknown target coordinates. By exploiting persymmetric structures, we propose an adaptive detector according to the principle of generalized likelihood ratio test. Numerical examples are provided to show that the robustness of the proposed detectors is better than that of their counterparts.