Square root decomposition based range-spread target subspace detector in interference-plus-noise background

The problem of range-spread target detection in the background of subspace interference plus unknown noise is studied. Both target and jamming are modeled as signals with unknown coordinates in a known subspace, and the target signal subspace is contained in the jamming signal subspace. A group of secondary data containing interference and noise is used to estimate the interference covariance matrix. The square root decomposition method is adopted to transform the interference matrix structure, and then the maximum likelihood estimation of unknown interference coordinate covariance matrix, noise power and target signal coordinates is resolved. Based on the one-step design strategy, a generalized likelihood ratio test detector is established. Theoretical analysis shows that the covariance matrix of the proposed detector for interference plus noise has a constant false alarm rate characteristic. Simulation analysis shows that the detection performance of the proposed range-spread target subspace detector is better than that of the existing comparison detector under the background of interference related to the target signal plus unknown noise, and it can effectively suppress the subspace interference signals of different powers.