【作者】 张一鸣； 王锐； 胡程；

【Author】 ZHANG Yiming;WANG Rui;HU Cheng;Radar Research Laboratory,School of Information and Electronics,Beijing Institute of Technology;

【机构】 北京理工大学信息与电子学院雷达技术研究所；

【摘要】 广义Radon-Fourier变换(GRFT)通过多维参数联合搜索实现长时间相参积累,能有效提升机动小目标的雷达检测性能。但其参数遍历搜索过程的运算量过大,且会跟随搜索精度的细化和搜索维度的增加而倍增。将GRFT变换转化为参数寻优问题,采用粒子群优化算法实现能够大幅减少搜索参数对,进而提升检测速度,但在性能上有较大损失。为此,通过改进算法中的关键参数及边界粒子的处理策略,提出了一种改进的基于粒子群优化的快速GRFT检测算法。在仿真实验中分析并验证了该算法的有效性。更多还原

【Abstract】 The Generalized Radon-Fourier Transform(GRFT) achieves long-time coherent integration via joint search in motion parameter space,which improves the radar detection performance of maneuvering weak targets.However,the ergodic multi-dimensional search suffers from heavy computational burden,and this problem will be more serious with the refinement of the search accuracy and the increase of the search dimension.The GRFT can be converted into an optimization problem in the parameter space,then the particle swarm optimization algorithm can greatly reduce the cost of parameters search,but there is a great loss in detection performance.Through the key parameters and the boundary strategy improving of the algorithm,an improved fast GRFT detection algorithm based on particle swarm optimization is proposed,and the effectiveness of this method is analyzed and verified through simulation experiments.更多还原