Improved Model-Based Rao and Wald Test for Adaptive Range-Spread Target Detection

This article addresses the problem of the detection of range-spread targets in the presence of Gaussian disturbance which are in possession of unidentified covariance matrices. The detectors have been derived by resorting to a design composed of two steps. Based on the Rao test and Wald test, the corresponding strategies of detection have been respectively derived very firstly, assuming the expression of disturbance covariance matrix has been obtained. Afterwards, the unknown parameters in the detectors have been estimated on the basis of both the primary and the training data, utilizing the autoregressive property of the disturbance. A remarkable characteristic of the Rao and Wald detectors is they both asymptotically attain constant false alarm rate(CFAR) in respect of the disturbance covariance matrix. Finally, we completed a performance assessment by utilizing the simulated data, and the result demonstrated the effectiveness of the existing proposals compared with the detectors previously proposed.