Robust Detection in MIMO Radar With Steering Vector Mismatches

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Robust Detection in MIMO Radar With Steering Vector Mismatches

Jun Liu; Jian Li

We address a robust detection problem for MIMO radars in Gaussian noise with unknown covariance matrix, for the mismatched case where the nominal transmit (or receive) steering vector may not be aligned with the true transmit (or receive) steering vector. Subspace models are adopted for taking into account these mismatches. More specifically, the transmit (or receive) steering vector is assumed to belong to a subspace. Without requiring training data, we design adaptive detectors according to the criteria of Wald test, Rao test, and generalized likelihood ratio test (GLRT). It is found that the Rao test does not exist for the detection problem to be solved. Additionally, the proposed Wald test and GLRT exhibit constant false alarm rate properties against the noise covariance matrix. Numerical examples show that the proposed detectors compared to their existing counterparts have better robustness against the steering vector mismatches. Moreover, the proposed Wald test exhibits better robustness than the proposed GLRT in the region of high signal-to-noise ratio.


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