二阶Radon-Fourier变换与遗传算法结合的快速相参积累算法

A Fast Coherent Integration Algorithm Based on Second-order Radon-Fourier Transform and Genetic Algorithm

针对匀加速运动的高速目标,可以用二阶Radon-Fourier变换(Second-order Radon-Fourier Transform,SRFT)完成对回波信号的相参积累。SRFT算法的原理是通过“速度-加速度”联合搜索来实现目标的运动参数估计,其计算量较大,不满足实时检测的需求。针对这个问题,提出一种基于遗传算法(Genetic Algorithm,GA)的快速实现方法。首先对运动参数集进行编码,设置初始群体;然后通过遗传算法对群体更新迭代,使其能够自发快速地逼近全局最优解,减少不必要的搜索路径;最终快速实现待检测目标的相参积累。仿真结果表明,在保证检测性能的前提下,算法计算量得到有效改善,运算次数减少大约一个量级。

The coherent integration of echo signals from high-speed targets undergoing uniform acceleration can be achieved by using the second-order Radon-Fourier transform(SRFT).However,the SRFT algorithm has high computational complexity,which is not suitable for real-time detection requirements.To overcome this limitation,the authors propose a fast implementation method based on the genetic algorithm(GA).Firstly,the motion parameters are encoded,and an initial population is generated.Then,the population is iteratively updated by using the genetic algorithm,allowing it to rapidly converge towards the global optimal solution and minimize unnecessary search paths.Finally,rapid phase accumulation of the target under detection is realized.Simulation results demonstrate that the proposed algorithm significantly reduces computational complexity while maintaining satisfactory detection performance,and the number of operations is reduced by about one order of magnitude.