Predictive Quantization for Data Volume Reduction in Staggered SAR Systems

Abstract

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.