Vector quantization for saturated SAR raw data compression

Spaceborne SAR involves the storage and transmission of large-size sampling data. Block adaptive quantization (BAQ) is now the most widely used onboard data compression algorithm due to its good tradeoff between system performance and complexity. However, when spaceborne SAR raw data is saturated, the performance of conventional BAQ deteriorates dramatically because its precondition of Gaussian distribution of raw data no longer holds. In order to solve this problem, an improved vector quantization (VQ) algorithm is proposed. This algorithm firstly introduces saturation modification to a conventional vector quantizer, obtains the saturation codebook based on Gaussian density function, and then obtains the new vector quantizer for the whole set of Saturation Degree (SD). This algorithm makes the vector quantizer match statistical model of data for the whole set of SD, so the performance of the compression is improved. The case of the 2D signal is explicitly computed. The performance of the proposed algorithm is verified by simulated and real data experiments.     

一种基于DCT的SAR图像自聚焦算法

研究了一种基于离散余弦变换(DCT, Discrete Cosine Transform)的SAR(Synthetic Aperture Radar)图像自聚焦算法.该方法从复图像域出发,通过在距离压缩相位历史域引入相位误差模型,改变图像的聚焦程度直至一维像的DCT序列在高序数区域的权值达到最大,从而完成误差校正.同相位梯度自聚焦算法相比,该方法无需在图像域分离出强点目标,因此特别适用于无任何明显特征的图像.由于DCT存在快速算法,使得该自聚焦算法计算量较少,更易实现.实测数据及仿真数据的成像结果证明了此方法的可行性.