Rao and Wald tests design for adaptive detection of range-spread targets in compound-Gaussian clutter

This paper considers the problem of detecting range-spread targets in compound-Gaussian clutter modeled as an autoregressive (AR) process with unknown parameters. Since no a uniformly most powerful test exists for this problem we design the model-based detection strategies based on Rao and Wald criterions respectively. The newly-proposed model-based detectors ensure the constant false alarm rate (CFAR) property with respect to the clutter power level, and are asymptotically CFAR with respect to the covariance matrix. The performance assessments, conducted by Monte Carlo simulations, have confirmed the effectiveness of the newly-proposed detectors. Moreover, the newly-proposed detectors have the same asymptotical performance as the two-step generalized likelihood ratio test with known covariance matrix.