Dead Range Gaps Estimation in Nisar S-BAND Sweep Sar

NISAR (NASA ISRO Synthetic Aperture Radar) is a joint SAR mission between NASA and ISRO having the capability to image a wide swath at high resolution using the SweepSAR technique. Dead range gaps are manifested in acquired raw data because of the large receive data window in comparison to PRI (Pulse Repetition Interval). The localized under-sampling because of dead range gaps (missing samples) combined with a non-uniform sampling of azimuth signal caused the aliasing of the azimuth spectrum. This results in the generation of ghost targets/ambiguities in processed data. The ambiguities are prominently visible in low backscatter areas lying adjacent to strong targets. The impact of aliasing can be mitigated by estimating the missing samples. This paper investigates two techniques namely BLU (Best Linear Unbiased) and MIAA (Missing Data Based Iterative Adaptive Approach) to estimate missing samples. The efficacy of techniques is studied using NISAR S-Band simulated targets and space-borne SAR data.