Persymmetric Adaptive Detection With Improved Robustness to Steering Vector Mismatches

We exploit persymmetry to study the adaptive detection problem with multiple observations in partially homogeneous environments where noise shares the same covariance matrix up to different power levels between the test and training data. A persymmetric subspace model is designed for taking into account steering vector mismatches. Based on the persymmetric subspace model, we propose adaptive detectors in partially homogeneous environments, according to the criteria of two-step generalized likelihood ratio test (GLRT), Wald test, and Rao test. It is found that the proposed GLRT and Wald test coincide, while the Rao test does not exist. The proposed detector is proved to exhibit a constant false alarm rate property against both the covariance matrix structure and the scaling factor. Numerical examples show that the proposed detector, compared to its counterparts, is more robust to steering vector mismatches.