针对传统星载滑动聚束合成孔径雷达(SAR)的方位分辨率存在较显著空变的问题，提出一种基于粒子群算法的星载斜视滑动聚束SAR扫描策略优化方法，该方法可以基本消除SAR图像中方位分辨率的空变，提高SAR图像用于目标识别时定位和识别的准确度。传统粒子群算法对扫描策略进行优化时易陷入局部最优，为此提出一种改进的粒子群算法，建立了一种滑动因子的参数化模型，使用粒子群算法求解该模型的参数，并将传统粒子群算法与邻域搜索算法结合，提高了算法搜索最优解的能力。仿真结果验证了所提方法的有效性。
龚力维
,
李飞
,
韩晓东
,
王伟
. 一种基于粒子群算法的星载斜视滑动聚束SAR扫描策略优化方法[J]. 中国科学院大学学报, 2025
, 42(3)
: 361
-370
.
DOI: 10.7523/j.ucas.2023.056
In view of the significant spatial variance in the azimuth resolution of traditional spaceborne sliding spotlight SAR, this paper proposes a scanning strategy optimizing method for spaceborne squint sliding spotlight SAR based on particle swarm optimization. The proposed method can basically eliminate the spatial variance of azimuth resolution in SAR images and improve the accuracy of localization and identification when SAR images are used for target identification. Considering that traditional particle swarm optimization is prone to fall into local optimum when optimizing the scanning strategy, this paper proposes an improved particle swarm optimization in which a parameterized model of the sliding factor, whose parameters are solved using particle swarm optimization, is established and a neighborhood search algorithm is integrated so that the ability of the algorithm to reach the optimum is enhanced. Simulation results validate the effectiveness of the proposed method.
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