Abstract
A new scheme is presented to estimate the range and azimuth velocity components of a detected moving target by using a dual-frequency synthetic aperture radar (SAR). It consists of a moving target detector, a range velocity estimator, and an azimuth velocity estimator. In this scheme, two original SAR images are achieved from the returns first, and then processed by a symmetric defocusing filter pair (SDFP) to produce two defocused images. By comparing the sharpness of the two defocused images, the moving targets are indicated and isolated form each original SAR image. For a selected moving target, its range velocity component is estimated by using a Doppler ambiguity solver and a stepped approximation-and-comparison algorithm. After range velocity compensated, the target in the patch is concentrated in less range bins, and its azimuth velocity component is estimated by using an SDFP bank. Finally, the moving target is refocused and its azimuth displacement caused by range velocity component is corrected. The effectiveness of the proposed scheme is confirmed by the experiments with the field and simulated data.
Highlights
Synthetic aperture radar (SAR) has been widely used in many civilian and military applications, and the SAR with ground moving targets indication (GMTI) is a very hot topic in recent years
If the returns from a moving target are processed in the same way as the stationary returns, the target will appear as an azimuth shift due to the range motion, and the image of the target will be smeared in the azimuth direction due to the azimuth motion [1]
The two original SAR images are processed by the MTD, and as a result, image patches that contain the detected moving targets are achieved
Summary
Synthetic aperture radar (SAR) has been widely used in many civilian and military applications, and the SAR with ground moving targets indication (GMTI) is a very hot topic in recent years. Moving target detection and velocity components estimation are the two main tasks of GMTI in SAR [2,3]. As detection methods are well-developed in many literatures, we will focus on the algorithms about estimation of velocity components. Many GMTI methodologies based on a single antenna SAR or a single complex-valued SAR image, e.g., auto-focusing [11], antenna beam patten transforming [12], and SAR stacks [13], were developed and got many effective results. The proposed methods suffer from either a high computation effort or unsatisfactory estimate accuracy under the condition of high signal-to-clutter-plus-interference-ratio
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