付小川,谢菊兰,何子述. 基于二重帕累托理论的共形极化阵同时发射多波束动态组阵[J]. 电波科学学报,2024,39(3):1-20. DOI: 10.12265/j.cjors.2023209
      引用本文: 付小川,谢菊兰,何子述. 基于二重帕累托理论的共形极化阵同时发射多波束动态组阵[J]. 电波科学学报,2024,39(3):1-20. DOI: 10.12265/j.cjors.2023209
      FU X C, XIE J L, HE Z S. Conformal polarization array with the simultaneous transmitting of multi-beam array optimization based on double Pareto theory[J]. Chinese journal of radio science,2024,39(3):1-20. (in Chinese). DOI: 10.12265/j.cjors.2023209
      Citation: FU X C, XIE J L, HE Z S. Conformal polarization array with the simultaneous transmitting of multi-beam array optimization based on double Pareto theory[J]. Chinese journal of radio science,2024,39(3):1-20. (in Chinese). DOI: 10.12265/j.cjors.2023209

      基于二重帕累托理论的共形极化阵同时发射多波束动态组阵

      Conformal polarization array with the simultaneous transmitting of multi-beam array optimization based on double Pareto theory

      • 摘要: 共形极化阵列各阵元的各向不一致性使得波束形成须根据波束指向进行组阵才能获得较好的功率增益合成。当阵列进行同时波束形成时如何通过组阵使得各波束性能都较好,复用阵元的归属是一个难点。针对这一问题,提出了一种基于二重帕累托理论的同时发射多波束动态组阵(simultaneous multi-beam dynamic array formation base on dual Pareto theory, SMDAF-DP)算法。该算法首先将基于帕累托最优理论的多目标粒子群优化(multi-objective particle swarm optimization, MOPSO)算法应用于共形极化阵,分别对每个波束指向进行组阵获得优选阵列;然后,针对各波束优选阵列中的复用阵元分配问题,提出了一种基于帕累托最优理论的多元粒子群优化(multivariate particle swarm optimization, MPSO)算法,通过实数优化的方式判断粒子位置,确定复用阵元最终的归属;最后,考虑波束指向分布疏散和密集的情况,对算法进行仿真验证。仿真结果表明:相较于现有算法,本文所提算法在保证阵元不复用的基础上能使各个波束形成更优的发射方向图。此外,在波束指向较为密集的情况下本文所提算法相比于现有算法仍具有更优越的性能,具有一定的稳健性。

         

        Abstract: The anisotropic inconsistency of each array element of the polarized conformal array makes the beamforming need to optimize the position of the array elements according to the beam pointing in order to obtain a better power gain synthesis. When the array performs simultaneous beamforming how to make the performance of each beam better by array element optimization, the attribution of the multiplexed array elements is a difficult point. In response to this issue, a simultaneous transmitting of multi-beam array optimization based on double Pareto (SMDAF-DP) algorithm is proposed. Firstly, the multi-objective particle swarm optimization algorithm based on Pareto optimality theory is applied to the conformal polarization array and the preferred array is obtained by optimizing the position of the array elements for each beam separately; Secondly, a multivariate particle swarm optimization algorithm based on Pareto optimality theory is proposed to solve the allocation of multiplexed array elements in preferred array for each beam. The final beam attribution of multiplexing array elements is determined by judging the particle positions by means of real number optimization; Finally, considering that the beam distribution is sparse and dense, the algorithm is simulated and verified. The simulation results show that, compared with the existing algorithms, the proposed method can get better transmitted patterns for each beam separately while the array elements are not multiplexed. In addition, the proposed method also has better performance and is more robust than existing algorithms in the case of dense beam pointing.

         

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