Extended GLRT Detection of Moving Targets for Multichannel SAR Based on Generalized Steering Vector.

Chong Song,Wei Li,Bingnan Wang,Maosheng Xiang

doi:10.3390/s21041478

Abstract

A generalized likelihood ratio test (GLRT) with the constant false alarm rate (CFAR) property was recently developed for adaptive detection of moving targets in focusing synthetic aperture radar (SAR) images. However, in the multichannel SAR-ground moving-target indication (SAR-GMTI) system, image defocus is inevitable, which will remarkably degrade the performance of the GLRT detector, especially for the lower radar cross-section (RCS) and slower radial velocity moving targets. To address this issue, based on the generalized steering vector (GSV), an extended GLRT detector is proposed and its performance is evaluated by the optimum likelihood ratio test (LRT) in the Neyman-Pearson (NP) criterion. The joint data vector formulated by the current cell and its adjacent cells is used to obtain the GSV, and then the extended GLRT is derived, which coherently integrates signal and accomplishes moving-target detection and parameter estimation. Theoretical analysis and simulated SAR data demonstrate the effectiveness and robustness of the proposed detector in the defocusing SAR images.

Highlights

As an advanced modern sensor that allows large area coverage in all-weather conditions during day and night, synthetic aperture radar (SAR) is widely applied in both civil and military fields [1,2]
To assess the detection and estimation performance of the proposed generalized steering vector (GSV)-generalized likelihood ratio test (GLRT), we presented the theoretical performance and the experimental results of simulated SAR data
The performance of the GSV-GLRT is compared with the traditional GLRT

Summary

Introduction

As an advanced modern sensor that allows large area coverage in all-weather conditions during day and night, synthetic aperture radar (SAR) is widely applied in both civil and military fields [1,2]. Due to the increase of the spatial degree of freedom, multichannel SAR (MSAR) can address the limit of velocity detection and the strong clutter interference in single-channel SAR [3,4] It can significantly improve the performance of moving-target detection, especially the ability to detect slow-moving targets. DPCA, ATI and EDPCA exploit the information contained in the current range-azimuth pixel only for moving-target detection. As a result, their performance is affected by the quality of complex SAR image sequences. Speaking, compared with the methods in the raw data domain, better performance can be obtained in the image domain due to the fact that the moving targets can be partially coherently integrated via the azimuth focusing [18]

Results

Discussion

Conclusion