Predictive Quantization for Staggered Synthetic Aperture Radar Systems

Abstract

In this master thesis, a data reduction method is investigated in the context of Tandem-L, a proposal of the German Aerospace Center (DLR) for a highly innovative L-band synthetic aperture radar (SAR) satellite mission to monitor the dynamic processes of the Earth. Tandem-L employs staggered PRI, a novel acquisition mode which allows for a swath width up to 350 km and an azimuth resolution in the order of 10 m, resulting in a huge required data volume of about 8 Terabyte per day, hence leading to hard requirements in terms of onboard memory and downlink capacity. For Tandem-L, a certain azimuth oversampling is mandatory in order to properly reconstruct the data in presence of the gaps introduced by the staggered SAR mode. The proposed technique takes advantage of the time variant autocorrelation properties of the non-uniform azimuth raw data stream in order to reduce the amount of data through a novel quantization method, named Predictive-Block Adaptive Quantization. Different prediction orders are investigated by considering the trade-off between achievable performance and complexity. Simulations for different target scenarios show that a data reduction of about 15% can be achieved with the proposed technique with a modest increase of the system complexity. Moreover, having a-priori information on the position of the gaps, a technique for their reconstruction based on dynamic bit allocation is proposed, showing no significant loss of information in correspondence of the missing azimuth samples.