Performance-Optimized Quantization for SAR and InSAR Applications

For the design of present and next-generation spaceborne SAR missions, constantly increasing data rates are being demanded, which impose stringent requirements in terms of onboard memory and downlink capacity. In this scenario, the efficient quantization of SAR raw data is of primary importance since the utilized compression rate is directly related to the volume of data to be stored and transmitted to the ground, and at the same time, it affects the resulting SAR imaging performance. In this article, we introduce the performance-optimized block-adaptive quantization (PO-BAQ), a novel approach for SAR raw data compression that aims at optimizing the resource allocation and, at the same time, the quality of the resulting SAR and InSAR products. This goal is achieved by exploiting the a priori knowledge of the local SAR backscatter statistics, which allows for the generation of high-resolution bitrate maps that can be employed to fulfill a predefined performance requirement. Analyses of experimental TanDEM-X interferometric data are presented, which demonstrates the potential of the proposed method as a helpful tool for performance budget definition and data rate optimization of present and future SAR missions

Predictive Quantization for Staggered Synthetic Aperture Radar Systems

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

Predictive Quantization for Data Volume Reduction in Staggered SAR Systems

Staggered synthetic aperture radar (SAR) is an innovative SAR acquisition concept which exploits digital beamforming (DBF) in elevation to form multiple receive beams and continuous variation of the pulse repetition interval to achieve high-resolution imaging of a wide continuous swath. Staggered SAR requires an azimuth oversampling higher than an SAR with constant pulse repetition interval (PRI), which results in an increased volume of data. In this article, we investigate the use of linear predictive coding, which exploits the correlation properties exhibited by the nonuniform azimuth raw data stream. According to this, the prediction of each sample is calculated onboard as a linear combination of a set of previous samples. The resulting prediction error is then quantized and downlinked (instead of the original value), which allows for a reduction of the signal entropy and, in turn, of the onboard data rate achievable for a given target performance. In addition, the a priori knowledge of the gap positions can be exploited to dynamically adapt the bit rate allocation and the prediction order to further improve the performance. Simulations of the proposed dynamic predictive block-adaptive quantization (DP-BAQ) are carried out considering a Tandem-L-like staggered SAR system for different orders of prediction and target scenarios, demonstrating that a significant data reduction can be achieved with a modest increase of the system complexity

Chirp Selection and Data Compression for Spaceborne Wide-Swath SAR in FScan-Mode

FScan has recently been proposed by Airbus DS as an appealing option to implement high-resolution wide-swath (HRWS) spaceborne SAR imaging. The concept uses the fanning-out beam pointing characteristic of phased array antennas operated at high bandwidth in a favorable way to steer the beam over a much wider swath as the nominal beamwidth would correspond. This paper treats the transmit pulse chirp length as a trade-off parameter and consequentially derives two variants of the FScan modes. The consequences on echo window length and the benefits of dedicated onboard processing for data volume reduction are discussed. In this context, the advantages of using a transform domain BAQ are pointed out