Eigenvalue‐based ground target detection in high‐resolution range profiles. Issue 11 (18th September 2020)

• Record Type: Journal Article
• Authors: Jiang, Yuan; Wang, Yan‐Hua; Li, Yang; Chen, Xing
• Is Part Of: IET radar, sonar & navigation. Volume 14:Issue 11(2020)
• Page Start: 1747
• Page End: 1756
• Publication Date: 2020-09-18
• Subjects: covariance matrices -- eigenvalues and eigenfunctions -- object detection -- radar resolution -- radar clutter -- signal detection -- radar detection -- target tracking -- matrix decomposition; eigenvalue‐based ground target detection -- high‐resolution range profiles -- range distributed target detection -- sequential high resolution range profiles -- modified scaled largest eigenvalue detector -- static target -- homogenous ground clutter -- secondary data -- target signal -- primary data -- sample covariance matrix -- SCM -- multiple HRRPs -- short coherent processing interval -- eigenvalue decomposition -- detection statistic -- detection performance -- coloured clutter -- clutter distribution -- descending order -- noise power estimation; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2020.0002
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : 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.