Eigenvalue-based ground target detection in high-resolution range profiles

摘要

In this study, the authors address the problem of range-distributed target detection in sequential high resolution range profiles (HRRPs). They propose a modified scaled largest eigenvalue detector for static target in homogenous ground clutter. A set of secondary data, which are free of target signal and have the same distribution as the clutter in the primary data, are assumed to be available. First, the sample covariance matrix (SCM) is estimated from the acquired multiple HRRPs in a short coherent processing interval. Then, the eigenvalue decomposition of the SCM is performed, and the eigenvalues are sorted in descending order. Finally, the largest eigenvalue scaled by the noise power estimated from the secondary data is selected as the detection statistic. Compared with existing methods of largest eigenvalue-based detection, the proposed method achieves better detection performance for coloured clutter by considering secondary data. Numerical and experimental results demonstrate the effectiveness of the proposed method.