Fast Moving Target Indication and Imaging in Stripmap SAR
Abstract This chapter mainly discusses the GMTI and GMTIm algorithms in stripmap SAR/GMTI mode. The signal model of fast moving targets in stripmap SAR/GMTI mode is established, and the impacts of fast cross-track velocities on the Doppler ambiguity and higher order azimuth phase are analyzed. Furthermore, a classification of targets by the locations of their spectra is presented. Based on the classification, a Doppler centroid estimation algorithm based on curve fitting, a multiple target indication and echo extraction method, and a fast moving target imaging algorithm based on Hough transform and third-order PFT are presented. Finally, simulations and real data are utilized to prove the effectiveness of these algorithms.
The stripmap mode is a fundamental and most widely-used working mode of the SAR system. High-resolution images of the observation area can be stably obtained in the stripmap mode since the antenna is fixed in this mode. Compared with the spotlight mode, the stripmap mode has a broader observation area, while compared with the scan-SAR mode, it has a higher resolution. Along with the development of the moving target processing technique, both the ability of moving target processing and the high resolution imaging are required in the stripmap SAR, therefore this new mode is called SAR/GMTI mode.
Chapter 2 of this book induced the broadside geometry of a moving target in the stripmap mode, and also some classical GMTI and GMTIm algorithms are introduced in Sects. 2.4 and 2.5. These algorithms are effective for the targets with certain scopes of motion parameters. As analyzed in Chapter Two, in the existence of the target with complex motions, such as Doppler ambiguity, these algorithms cannot accurately indicate, estimate and focus the moving target. In the real SAR signal processing, the motions of the moving target is unknown, the possibility of the existence of Doppler ambiguity cannot be ignored, therefore it is necessary to develop the fast moving target indication and imaging algorithms.
© Springer Nature Singapore Pte Ltd. 2017
J. Yang, Study on Ground Moving Target Indication and Imaging Technique of Airborne SAR, Springer Theses, DOI 10.1007/978-981-10-3075-8_3
In order to solve the fast moving target indication and imaging in stripmap SAR, an adaptive Doppler centroid estimation algorithm is introduced in this chapter. By using the curve fitting of the azimuth spectrum, the Doppler centroid can be accurately estimated in the non-homogeneous scene, and the impact of the prominent targets is eliminated. With the accurate estimation of the Doppler centroid, the Doppler centroid of the stationary scene can be adjusted into zero, which is the foundation of the following moving target processing algorithms.
After adjusting the Doppler centroid error of the clutter, a multiple moving target indication and echo extraction method is proposed. This proposed two-step algorithm indicates the moving targets with different motion parameters based on the classification in Sect. 2.3.2, which can indicate the fast moving target submerged by the clutter. After GMTI, the impacts of the integrity and SNR of the echo extraction on the following parameter estimation and imaging algorithms are discussed.
Then, a fast moving target imaging algorithm based on Hough transform and third-order PFT is presented. This algorithm can accurately estimate the real Doppler centroid in the existence of the Doppler ambiguity. And the third-order azimuth phase error is compensated by using the third-order PFT. The effectiveness of the algorithm is sufficiently proved by simulations and real data processing.
This chapter is organized as follows. Based on Sect. 2.3, the echo model of moving targets with fast cross-track velocity is established in Sect. 3.2. An adaptive Doppler centroid estimation algorithm is introduced in Sect. 3.3, and a two-step multiple moving target indication and echo extraction method is proposed in Sect. 3.4. Then, a fast moving target imaging algorithm based on Hough transform and third-order PFT is introduced in Sect. 3.5. Finally, conclusive remarks are provided in Sect. 3.6.