Improved model-based Rao test for adaptive range-spread target detection in complex Gaussian clutter

摘要

In this study, we mainly focus on the adaptive detection of range-spread targets in the context of compound Gaussian clutter, which is in possession of unknown covariance matrix. With the purpose to overcome the problem of performance degradation which is principally triggered by the limitation of training data number, the autoregressive process is applied to model the speckle component. Firstly, the form of Rao test is derived under the assumption of known covariance matrix of the clutter, afterwards the covariance matrix is reconstructed by AR parameters resorting to matrix factorization. The newly derived detector is proved asymptotically constant false alarm rate in respect of the clutter covariance matrix, and the simulation results have demonstrated the effectiveness of the new detector.