| 摘要: |
| 稀疏阵列优化通过抑制阵列天线旁瓣电平，可增强天线系统的空间分辨率、抗干扰能力等。目前多采用智能阵列优化算法，但算法中常存在着收敛效率低和局部最优解等问题。针对上述问题，提出了一种基于循环回溯寻优的稀疏阵列优化算法。该算法以不同阵元数量的阵列方向图作为解搜索空间，以最低峰值旁瓣电平（Peak Sidelobe Level，PSLL）作为搜索择优标准，从阵元数量较少阵列状态（低阶态）搜索到阵元数量较多阵列状态（高阶态）后再回到低阶态，每次寻优用当前最优解代替历史最优解，使得PSLL趋近于全局最优解。仿真实验表明，在同等实验条件下，相比于其他优化算法，所提算法降低了PSLL 0.23~5.71 dB；在相同迭代次数下，相较其他优化算法减少了0.88~1.65 s寻优时间。 |
| 关键词: 稀疏阵列优化 模拟退火算法 循环回溯优化 |
| DOI：10.20079/j.issn.1001-893x.241216001 |
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| 基金项目:中国电子科技集团公司第十二研究所稳定支持科研经费资助项目 |
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| Sparse Array Optimization Based on Iterative Backtracking Optimization Algorithm |
| YU Detao,CAI Jingjing,WANG Feixiang,LI Wenxu |
| (1.School of Electronic Engineering，Xidian University，Xi’an 710071，China;2.School of Information System Engineering，Information Engineering University，Zhengzhou 450001，China;3.The 12th Research Institute of China Electronics Technology Group Corporation，Beijing 100015，China) |
| Abstract: |
| The sparse array optimization enhances the spatial resolution and anti-interference ability of the antenna system by depressing the side-lobe level of the array antenna.The intelligent array optimization algorithms are mostly used nowadays，but they often suffer from low convergence efficiency and local optimization solution problems.For above problems，a sparse array optimization algorithm based on the iterative backtracking strategy is proposed.The proposed algorithm takes the array patterns of arrays with different numbers of elements as the solution searching space and the lowest peak sidelobe level(PSLL) as the optimization criterion of searching，and searches from the array status of less array elements(low-order status) to the array status of more array elements(high-order status)，and then back to the low-order status.The historical optimal solution is always replaced by the current optimal solution in each round of optimization，which makes the PSLL approach the global optimal solution.Simulations prove that，the proposed algorithm decreases the PSLL，0.23 dB to 5.71 dB compared with other optimization algorithms in the same simulation conditions.Furthermore，it decreases the optimization time 0.88 s to 1.65 s compared with other optimization algorithms in the same number of iterations. |
| Key words: sparse array optimization simulated annealing algorithm iterative backtracking optimization |
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