基于忆阻RBF神经网络的MIMO雷达抗干扰波形设计

Anti-jamming Waveform Design Based on Memristive RBF Neural Network in MIMO Radar

转发式欺骗干扰是基于数字射频存储技术(DRFM)的新型脉冲雷达干扰手段,干扰信号经过雷达接收机匹配滤波后可在真实目标前后产生多个假目标,影响雷达对真实目标进行探测和跟踪。针对转发式欺骗干扰问题,提出了一种基于忆阻RBF神经网络的MIMO雷达频率-相位联合捷变的抗干扰波形设计方法。首先,以发射信号脉冲间互相关峰值电平和各脉冲自相关峰值旁瓣比为目标函数,由波形模糊函数推导了波形优化数学模型。然后,采用忆阻RBF神经网络构建混沌动力学系统对目标函数进行优化,解决了传统波形优化算法时间复杂度较高的问题。最后,通过仿真比较了所提算法与遗传算法的时间复杂度,并验证了所提算法对转发式欺骗干扰具有较好的抑制性能。

Repeater deception jamming is a new type of pulse radar jamming method based on digital radio frequency memory technology. After the jamming signal is matched and filtered by the radar receiver,multiple false targets can be generated before and after the real target, which affect the radar’s detection and tracking of the real target. Aiming at the problem of repeater deception jamming, a MIMO radar frequencyphase joint agility waveform design method based on memristor RBF neural network is proposed. Firstly,the mathematical model of waveform optimization is derived from the waveform ambiguity function with the peak cross-correlation level between the transmitted signal pulses and the auto-correlation peak sidelobe level of each pulse as the objective function. Secondly, the memristive RBF neural network is used to construct a chaotic dynamic system to optimize the waveform optimization objective function, which solves the problem of high time complexity of traditional waveform optimization algorithms. Finally, simulations are presented to compare the time complexity of the proposed algorithm and genetic algorithm, and demonstrate that the proposed algorithm has good performance to against the repeater deception jamming.