This chapter mainly focuses on the non-ideal motion error estimation and compensation in real SAR data GMTIm. The existing GMTIm algorithms have been proved valid and effective in simulations, but in real data processing, their performances are degraded. It indicates that there are non-ideal motion errors that must be compensated in the real data processing.
Two sorts of non-ideal motion errors have been analyzed, and the estimation and compensation algorithms have been presented. Simulations and real data have been utilized to prove the effectiveness of the algorithms.
The main contribution of this chapter is to analyze the impact of the Doppler centroid error on the imaging of moving targets. This research is still on the exploratory stage, while it appears to be inspirational in the GMTIm with real SAR data. Along with following researches, there may be other non-ideal errors, and the GMTIm of real SAR data will be further improved.
- 1. Li Y, Liu C, Wang Y et al (2012) A robust motion error estimation method based on raw SAR data. IEEE Trans Geosci Remote Sens 50(7):2780-2790
- 2. Chen L, Liang X, Ding C (2010) Non-uniform reconstruction method in SAR imaging. J Syst Simul 22(5):1242-1245
- 3. Zhang Z (2003) Introduction to airborne and spaceborne synthetic aperture radar. Publishing House of Electronics Industry, Beijing
- 4. Wahl DE, Eichel P, Ghiglia DC et al (1994) Phase gradient autofocus—a robust tool for high resolution SAR phase correction. IEEE Trans Aerosp Electron Syst 30(3):827-835
- 5. Grewal MS, Weill LR, Andrews AP (2007) Global positioning systems, inertial navigation, and integration. Wiley, Hoboken
- 6. Chen Q, Li J (2004) Performance analysis and improvement of phase gradient autofocus algorithm. J Beijing Univ Aeronaut Astronaut 30(2):131-134
- 7. Zhao X, Wang X, Wang Z (2005) Phase gradient autofocus algorithm for SAR imagery based on contrast criteria. Remote Sens Technol Appl 20(6):606-610
- 8. Eichel PH, Jakowatz CV Jr (1989) Phase-gradient algorithm as an optimal estimator of the phase derivative. Opt Lett 14(20):1101—1103
- 9. Jakowatz CV Jr, Wahl DE (1993) Eigenvector method for maximum-likelihood estimation of phase errors in synthetic-aperture-radar imagery. J Opt Soc Am 10(12):2539-2546
- 10. Tsakalides P, Nikias CL (2011) High resolution autofocus techniques for SAR imaging based on fractional low-order statistics. In: IEE proceedings-radar, sonar and navigation, October 2011, vol 148, no 5, pp 267-276
- 11. Tsao J, Stenberg BD (1988) Reduction of sidelobe and speckle artifacts in microwave imaging: the CLEAN technique. IEEE Trans Antennas Propag 36(4):543-556