AR-model-based adaptive detection of range-spread targets in compound Gaussian clutter

In this paper, we consider the problem of adaptive detection for range-spread targets with known Doppler and unknown complex amplitude in compound Gaussian clutter. The speckle component of the clutter is modeled as an autoregressive (AR) process. By using the generalized likelihood ratio test (GLRT) approach, we will first estimate the AR parameters and the unknown complex amplitude, and then propose an adaptive AR-based GLR detector. The performance assessments are presented too. The computer simulations show that the proposed detector, without a priori information of the covariance matrix, has the same asymptotical performances as the two-step GLR-based detector with known covariance matrix.

Research highlights
► Give the AR-process description for compound Gaussian-distributed data vector;
► Derive the asymptotically optimum detector in the compound Gaussianclutter without secondary data;
► The numerical results show that the new detector has good rejection performance.