Fusion of dynamic predictive block adaptive quantization and vector quantization for staggered SAR data compression

Abstract

ABSTRACT Staggered synthetic aperture radar (SAR) is an advanced concept for high-resolution wide-swath (HRWS) imaging, which overcomes the problem of blind ranges by continuously varying the pulse repetition frequency (PRF). For better performance of information recovery in blind ranges, data are typically highly oversampled in azimuth for staggered SAR system. Thus a huge data volume onboard is required and the demands for internal data storage and downlink capacity are increasing. In this paper, we investigate a novel method, named Dynamic Predictive Block Adaptive Vector Quantization (DP-BAVQ), to reduce the volume of downlinked data for staggered SAR. It firstly compresses the difference of the raw data and their predictions with the dynamic predictive block adaptive quantization (DP-BAQ). Then a secondary compression is performed with vector quantization (VQ). The simulation results and the experiments with real data show that the proposed method achieves an improvement in the signal-to-quantization noise ratio (SQNR) compared with DP-BAQ. Moreover, DP-BAVQ at 2 bits/sample provides a higher SQNR compared with standard block adaptive quantization (BAQ) at 3 bits/sample. Thus the proposed method allows for a significant reduction of data volume.