Robust detection of distributed targets based on Rao test and Wald test

Abstract

We consider the problem of detecting distributed targets in Gaussian noise with unknown covariance matrix. To make the signal-plus-noise hypothesis more plausible in the mismatched case, we model the received signal under the signal-plus-noise hypothesis as the sum of noise, useful target echoes and fictitious signals. Two adaptive detectors are designed according to the Rao test and Wald test. We prove the proposed Rao test and Wald test exhibit constant false alarm rate properties against the covariance matrix. Numerical examples show that the proposed Rao test has strong robustness against the steering vector mismatches.