| 引用本文: | 洪升， 付勇强， 李铭晖， 张妤歆. 基于CIA的雷达互补稀疏频率序列集设计[J]. 雷达科学与技术, 2022, 20(6): 606-613.[点击复制] |
| HONG Sheng, FU Yongqiang, LI Minghui, ZHANG Yuxin. Design of Complementary Sparse Frequency Sequence Set in Radar Based on CIA[J]. Radar Science and Technology, 2022, 20(6): 606-613.[点击复制] |
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| 摘要: |
| 互补序列集(Complementary Sets of Sequences, CSS)具备良好的自相关性能，但在频谱受限的条件下，自相关性能恶化，因此有必要对频谱受限的CSS，即互补稀疏频率(Complementary Sparse Frequency, CSF)序列集，进行优化设计。本文以加权积分旁瓣电平(Weighted Integral Sidelobe Level, WISL)表征序列集的自相关性能，以频率阻带内的能量(Energy in the Frequency Stopband, EFS)表征序列集的稀疏频谱性能；并将二者的加权和作为目标函数，以序列集为优化变量，在恒模的约束下，建立联合优化问题。针对该优化问题，采用循环迭代算法(Cyclic Iterative Algorithm, CIA)求解，引入辅助变量，将辅助变量和序列集变量迭代求解。由于在每次迭代过程中都可求得序列集的恒模闭式解，相比于其他的CSF序列集优化算法，CIA收敛速度快，所设计CSF序列集的相关特性和频谱特性好，且可抑制指定范围内的自相关旁瓣。仿真结果验证了所提算法的有效性和优异性。 |
| 关键词: 互补序列集 稀疏频率 旁瓣抑制 循环迭代算法 |
| DOI：DOI:10.3969/j.issn.1672-2337.2022.06.002 |
| 分类号:TN957.3 |
| 基金项目:国家自然科学基金（No.61661032）; 中国博士后科学基金（No.2017M622102）； 江西省博士后科研项目（No.2017KY36）； 江西省自然科学基金（No.2016BAB212038, 20181BAB202002） |
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| Design of Complementary Sparse Frequency Sequence Set in Radar Based on CIA |
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HONG Sheng, FU Yongqiang, LI Minghui, ZHANG Yuxin
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School of Information Engineering， Nanchang University， Nanchang 330031， China
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| Abstract: |
| Complementary sets of sequences (CSS) have good auto-correlation performance, but this performance degrades when the spectrum is limited. Therefore, it is necessary to design the optimal CSS with a limitedspectrum, i.e., the complementary sparse frequency (CSF) sequence set. In this paper, the weighted integral sidelobe level (WISL) is used to characterize the auto-correlation performance of the sequence set, while the energy in the frequency stopband (EFS) is used to characterize the sparse spectrum performance of the sequence set. Taking the weighted sum of them as the objective function and the sequence set as the optimization variables, the joint optimization problem is formulated under the constraint of constant modulus. Aiming at the optimization problem, the cyclic iterative algorithm (CIA) is used to solve it. The auxiliary variables are introduced, and they are solved iteratively along with the sequence set variables. In each iteration process, a closed-form solution for the CSS with constant modulus is obtained. The CIA has a fast convergence speed when compared with other optimization algorithms, and the designed CSF sequence set has good correlation and spectral characteristics as desired. The CIA can also suppress the auto-correlation sidelobe in the specified range. The simulation results verify the effectiveness and superiority of the proposed algorithm. |
| Key words: complementary sets of sequences (CSS) sparse frequency sidelobe suppression cyclic iterative algorithm |
