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.

摘要

交错式合成孔径雷达（SAR）是高分辨率宽频（HRWS）成像的高级概念，它通过连续改变脉冲重复频率（PRF）来克服盲区问题。为了在盲范围内更好地恢复信息，对于交错SAR系统，通常会在方位角上对数据进行高度过采样。因此，机载需要巨大的数据量，并且对内部数据存储和下行链路容量的需求正在增加。在本文中，我们研究了一种称为动态预测块自适应矢量量化（DP-BAVQ）的新方法，以减少交错SAR的下行数据量。它首先利用动态预测块自适应量化（DP-BAQ）压缩原始数据及其预测的差异。然后使用矢量量化（VQ）进行二次压缩。仿真结果和实际数据实验表明，与DP-BAQ相比，该方法在信噪比（SQNR）上有较大的提高。此外，与3位/采样的标准块自适应量化（BAQ）相比，2位/采样的DP-BAVQ提供了更高的SQNR。因此，所提出的方法允许显着减少数据量。