Efficient Array Pattern Synthesis via Principal Angle-Based Sparse Sampling (PABSS)

This article presents the principal angle-based sparse sampling (PABSS) method, developed to enhance the efficiency of array pattern synthesis by streamlining the angular samples. Through the analysis of the correlation between array manifold components using principal angles, PABSS identifies and removes highly correlated samples, significantly lowering the computational complexity of synthesis, which is especially beneficial for large-scale arrays. As the sparsification process is based on the intrinsic properties of the array manifold, the sparse samples generated by PABSS can be reused across different synthesis tasks. Comprehensive numerical examples showcase the flexibility and effectiveness of PABSS when integrated with classical synthesis techniques, including beam scanning, beam shaping, and polarization synthesis for linear, planar, and conformal arrays. The results consistently demonstrate that PABSS provides substantial efficiency improvements while maintaining satisfactory synthesis accuracy.