基于高斯分布判决的SAR原始数据压缩方法

针对合成孔径雷达(SAR)数据概率密度函数偏离高斯分布时分块自适应量化(BAQ)压缩算法性能下降的问题,提出一种判断SAR数据是否符合高斯分布的新方法,并将其应用于SAR原始数据压缩。该方法借助几何关系将高斯曲线用等面积的三角形替代,计算出二者的交点坐标,并对各交点范围内二者的积分值进行比较,得到三个判决条件;然后,将满足这些条件的SAR数据判断为标准高斯分布数据并进行BAQ压缩,对判断为非高斯分布的数据采用矢量量化方法压缩。最后,实测数据的仿真实验说明本文方法的性能优于BAQ和文献[9]的方法。

A SAR Raw Data Compression Algorithm Based on Gaussian Distribution Judgment

In order to improve the performance of block adaptive quantization (BAQ) when some SAR data deviate from Gaussian distribution, an algorithm judging whether the data satisfies Gaussian distribution is proposed and applied in SAR raw data compression. The judging algorithm uses the geometrical relationship between standard Gaussian curve and a triangle whose area is equal to that of the Gaussian curve, then the coordinates of the intersection of two curves are computed and the integral values within each node of two curves are compared to form three judgment conditions. After that, the data satisfying these conditions is compressed by BAQ, otherwise compressed by VQ. Experimental results indicate that the performance of the proposed algorithm is superior to BAQ and reference[9] method.