直反信号协同的GNSS-R BSAR距离多普勒成像算法

GNSS-R BSAR range-Doppler imaging algorithm based on synchronization of direct and echo signal

针对目前基于全球导航卫星系统反射信号的双基地合成孔径雷达(GNSS-R BSAR)在一站固定模式下的大斜视,斜距历程复杂,回波信号方位空变导致回波信号难以处理的问题,提出改进的距离多普勒成像新算法。所提算法采用GNSS信号作为辐射源,根据一站固定模式下GNSS-R BSAR合成孔径时间长的特点,引入高阶等效斜视距离模型,得到导航卫星与目标斜距相对时间变化的精确描述。先通过直射信号与回波信号时域对消进行距离徙动校正,实现全场景目标距离徙动的精确校正;再通过方位向分块混合相关处理来克服回波信号方位向的移变性质,实现全场景高效精确成像。所提算法的成像效率优于传统后向投影时域(BP)算法,成像精度与BP算法相当,且可根据需要通过调整方位分块的宽度来提升聚焦效果。最后,用GPS-L5信号进行仿真和实验,仿真和实验结果验证了所提算法的可行性和高效性。

Aiming at the problems that the current global navigation satellite system-reflectometry bistatic synthetic aperture radar(GNSS-R BSAR)has a large squint in the fixed mode of one station,the slant range history is complicated,and the echo signal's azimuth is changed,the echo signal is difficult to process,an improved Range Doppler imaging algorithm is proposed.The method uses GNSS signal as the radiation source,and introduces a highorder squint range model based on the long GNSS-R BSAR synthetic aperture time in the one-stop fixed mode to obtain an accurate description of the relative time variation of the squint range between the navigation satellite and the target.Based on this model,firstly,the range migration is corrected by the time-domain cancellation of the direct signal and the echo signal to realize the accurate correction of the target range migration in the whole scene;By azimuth-block hybrid correlation processing,the azimuth shifting nature of the echo signal is overcome,and efficient and accurate imaging of the whole scene is realized.The imaging efficiency of the proposed algorithm is better than that of the traditional BP algorithm,the imaging accuracy is comparable to that of the back projection(BP)algorithm,and the focusing effect can be improved by adjusting the width of the orientation bins as needed.Finally,to validate the proposed algorithm,we conducted simulations and experiments with GPS-L5 signals,the simulation and experimental results verified the feasibility and efficiency of the proposed algorithm.