间隔和幅相联合优化的密集假目标干扰算法

Dense False Target Jamming Algorithm with Joint Interval and Amplitude Phase Optimization

针对典型密集假目标干扰排列形式简单、易于识别等问题,在设置密集假目标区域和稀疏假目标区域的基础上,设计了间隔和幅相联合优化的密集假目标(Interval-Phase-Amplitude Optimization Dense False Target,IPAO-DFT)干扰模型。提出了基于网格自适应直接搜索算法(Mesh Adaptive Direct Search Algorithms,MADS)的密集假目标干扰波形优化算法。以恒虚警率(Constant False-Alarm Rate,CFAR)检测门限均值和峰值功率作为目标函数和约束条件,形成了多参数、多约束的混合整数优化密集假目标干扰模型。仿真表明,与相同峰值功率的密集假目标干扰相比,基于MADS算法的IPAO-DFT干扰模型使CFAR检测门限均值提高了3 dB以上。

Aiming at the problems of simple arrangement and easy identification of typical dense false target interference,an interval phase amplification optimization dense false target(IPAO-DFT)interference model is designed,based on the setting of dense false target area and sparse false target area.A dense false target interference waveform optimization algorithm based on mesh adaptive direct search(MADS)algorithms is proposed.The constant false-alarm rate(CFAR)detection threshold mean and peak power are used as the objective function and constraints respectively.A multi-parameters and multi-constraints mixed integer optimization dense false target interference model is formed.The simulation results show the comparison of the dense false target interference with the same peak power and the IPAO-DFT interference model based on MADS algorithm.The proposed method has the ability to increase the mean of CFAR detection threshold by more than 3 dB.