Rao Test With Improved Robustness for Range-Spread Target Detection

This paper deals with the problem of detecting range-spread targets in Gaussian noise with unknown covariance matrix. We model the received signal under the signal-plus-noise hypothesis as the sum of noise, useful target echoes and fictitious signals, which makes the signal-plus-noise hypothesis more plausible in the mismatched case. An adaptive detector is designed according to the Rao test. We prove the proposed Rao test exhibits constant false alarm rate property against the covariance matrix. Numerical examples show that the proposed Rao outperforms its counterparts in the mismatched case.