单脉冲相控阵列天线由于可以实现快速的电子波束扫描且具有较高的测角精度而被广泛应用于跟踪雷达。在实际的工程应用中，阵列误差的存在是无法避免的。这些阵列误差会对天线的辐射特性产生较大的影响，尤其是会增加天线的副瓣电平，从而影响单脉冲和差波束的测角精度以及雷达的抗干扰性能。因此，为了减少阵列误差对副瓣电平的影响，本研究主要利用了两种仿生学优化算法完成了存在阵列误差情况下的低副瓣和差波束设计。本文的主要工作可以归纳为：

1.研究了存在阵列误差情况下基于遗传算法的低副瓣和差波束设计。首先，针对实际情况中存在的阵列误差进行了讨论和分析。在此基础之上提出了一种更为广义的阵列误差信号模型，并探究了不同阵列误差对天线副瓣电平的影响。其次，针对复杂非线性优化的低副瓣阵列综合问题，分别建立了相应的和、差波束阵列误差信号模型。将求解出的基于遗传算法的最优激励权值在三组不同阵列误差下进行低副瓣和、差波束形成。其中，和波束的副瓣电平分别降低了约7.28dB、7.20dB和8.01dB，差波束的副瓣电平分别降低了约5.56dB、5.23dB和4.97dB。最后，利用HFSS电磁仿真软件建立了1×8阵元的微带天线阵列模型。将综合得到的阵列误差下基于遗传算法的和、差波束最优激励权值应用于该模型中。仿真结果表明在面对阵元间强相互耦合的作用时，和、差波束的副瓣电平分别减少了约2.2dB和3.0dB。进一步验证了所设计的方法在实际情况中进行低副瓣和差波束形成时的有效性。

2.研究了存在阵列误差情况下基于粒子群优化算法的低副瓣和差波束设计。首先，针对阵列误差的影响以及非线性优化的低副瓣阵列综合问题，分别建立了阵列误差下的和、差波束信号模型。然后，考虑三组随机阵列误差的情况，将这两个信号模型下求解得到的基于粒子群优化算法的最优激励权值应用于低副瓣和、差波束形成。仿真结果表明和波束的副瓣电平分别减少了约5.41dB、5.88dB和7.04dB，差波束的副瓣电平分别减少了约7.82dB、6.17dB以及7.12dB。最后，将综合得到的阵列误差下基于粒子群优化算法的最优激励权值应用于相同的HFSS阵列天线模型中，分别进行和、差波束形成。验证结果表明和、差波束的副瓣电平分别减少了约3.9dB和5.9dB。进一步证明了该设计方法在阵元间强互耦影响下进行低副瓣和差波束形成时的有效性。

Monopulse phased array antennas are widely used in tracking radar. In practical scenario, the existence of array errors cannot be avoided, which will increase the side lobe level (SLL) of antenna and decrease the anti-interference performance of radar. To reduce the influence of array errors on the SLL, two bionic optimization algorithms are mainly used to complete the design of sum-difference beampatterns with low SLLs in the presence of array errors. The main contributions of this research can be summarized as:

1. The design of sum-difference patterns with low SLLs based on genetic algorithm (GA) in the presence of array errors is proposed. Firstly, a more generalized array error signal model is proposed to research the influence of different array errors on SLLs. Secondly, the corresponding signal models are established respectively to calculate the optimal excitation weights based on GA, which are utilized in sum-difference beamforming with low SLLs under different array errors. The simulation results show that the low SLLs in the presence of array errors can be achieved with the proposed approach. Finally, electromagnetic simulation using HFSS workbench is employed to testify the effectiveness of the proposed method.

2. The design of sum-difference patterns with low SLLs based on particle swarm optimization (PSO) algorithm in the presence of array errors is proposed. The corresponding signal models are established respectively to calculate the optimal excitation weights based on PSO, which are utilized in sum-difference beamforming with low SLLs in the presence of different array errors. The results illustrate that the low SLLs can be achieved. Electromagnetic simulation using HFSS workbench is employed to further testify the effectiveness of the proposed approach.