合成孔径雷达定点CS算法量化噪声分析

为了实现星载合成孔径雷达(synthetic apertureradar,SAR)成像处理器小型化和实时信号处理,该文重点研究定点运算和有限字长存储引入的量化噪声。根据chirpscaling (CS)成像算法,建立了CS算法定点运算的量化误差模型,分析了处理流程中的量化噪声,推导了系统输出噪信比与系统字长、FFT长度等参数之间的关系。采用不同系统字长对Radarsat-I数据成像,图像质量分析与所述理论一致,结果表明:通过计算处理流程的噪信比,可实现定点SAR成像处理器系统字长等关键参数的设计。

Quantization noise anlysis of fixed-point CS algorithm for SAR

Quantization noise from fixed-point processing and limited word length storage affect real-time imaging and system miniaturization in spaceborne synthetic aperture radar(SAR) imaging processors. A quantization error model was developed for fixed-point SAR processing using the well-known chirp scaling(CS) algorithm. The expression of noise-to-signal(NSR),system word length,and FFT length was deduced.Test images from the raw Radarsat-I data were then analyzed using the fixed-point CS algorithm with various system word lengths.The image quatilities are consistent with theoretical values. The analysis shows that the key parameters,such as the system word length,of fixed-point SAR imaging proessors may be determined by calculating the total NSR of the system outputs.