Journal
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
Volume 58, Issue 4, Pages 2900-2915Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAES.2022.3141332
Keywords
Clutter; Trajectory; Target tracking; Synthetic aperture radar; Radar tracking; Radar imaging; Signal to noise ratio; Circular synthetic aperture radar (CSAR); ground moving indication (GMTI); relaxation-based cyclic (RELAX); trajectory reconstruction
Funding
- National Natural Science Foundation of China [62101562]
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In this article, a framework of multichannel SAR-GMTI is proposed to address the detection of slow-moving targets buried in strong clutter. The method includes clutter suppression, moving target tracking, and trajectory reconstruction steps, demonstrating wide applicability and effectiveness.
Synthetic aperture radar ground moving target indication (SAR-GMTI) is an attractive mode for radar system to obtain high-resolution regional images and detection of moving targets simultaneously. Moreover, the 360 degrees observation capability of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. In order to address the problem that slow-moving targets, which are buried in strong stationary ground clutter, are difficult to be detected, a framework of multichannel SAR-GMTI is presented in this article. For ground moving target detection in the presence of stationary clutter, the proposed method first adopts clutter suppression interferometry and relaxation-based cyclic algorithm to suppress clutterand retrieve parameters (e.g., radial velocity and Doppler information). Then, to further reduce the false alarm rate, multiple moving target tracking algorithm is performed in range-Doppler domain. Finally, the moving target trajectory is reconstructed by using the proposed two-stage parameter estimation method based on Doppler characteristic and CSAR geometry, and the mathematical analysis of this method is performed. The proposed method is robust and insensitive to the signal-to-noise ratio. It does not rely on priori road information, so it has wide applicability, especially in military application. In addition, the framework is performed in echo domain and independent of imaging processing. The experimental results on simulated data are presented to evaluate the estimation accuracy of the proposed method, and the real data processing results are provided to demonstrate the validity and feasibility of the proposed method.
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