Persymmetric subspace adaptive detection and performance analysis

Abstract

This study deals with the problem of detecting a subspace signal in coloured Gaussian noise, where the subspace signal belongs to a known subspace, but with unknown coordinates. The authors exploit the persymmetric structure of the covariance matrix by a unitary transform and then devise a persymmetric subspace detector based on two-step design procedure. By exploiting the persymmetric structure of the covariance matrix, the proposed detector can reduce training data requirements. Additionally, approximate expressions for the probabilities of false alarm and detection of the proposed detector are derived. Numerical results demonstrate that the proposed detector can offer significantly enhanced detection performance in comparison with the conventional counterparts when the amount of training data is limited.

Keywords

• covariance matrices
• gaussian noise
• signal detection
• radar detection
• adaptive signal detection
• probability
• unknown coordinates
• persymmetric structure
• covariance matrix
• persymmetric subspace detector
• two-step design procedure
• training data requirements
• false alarm
• enhanced detection performance
• performance analysis
• subspace signal
• coloured gaussian noise
• known subspace