Compressive Sensing Based Design of Sparse Tripole Arrays.

Area of Science:

• Electromagnetics and Antenna Theory
• Signal Processing
• Array Signal Processing

Background:

• Tripole antenna arrays offer full polarization measurement capabilities.
• Designing sparse arrays is crucial for efficiency and reduced element count.
• Compressive Sensing (CS) provides a framework for sparse recovery.

Purpose of the Study:

• To formulate the design of sparse linear tripole arrays as an optimization problem.
• To develop a method ensuring complete tripoles are minimized at each location, achieving true sparsity.
• To verify the effectiveness of the proposed design methods through examples.

Main Methods:

• Formulation of the sparse tripole array design problem using Compressive Sensing (CS).
• Application of modified l1 norm minimization for simultaneous weight coefficient minimization.
• Utilization of iteratively reweighted minimization techniques for guaranteed sparsity.

Main Results:

• A novel optimization framework for sparse tripole array design.
• Demonstration of achieving a reference pattern with fewer tripoles compared to Uniform Linear Arrays (ULAs).
• Validation of the proposed methods through design examples.

Conclusions:

• The proposed Compressive Sensing-based approach effectively designs sparse tripole arrays.
• The method achieves true sparsity by minimizing complete tripoles, not individual dipoles.
• Sparse tripole arrays offer a more efficient alternative to ULAs for achieving desired radiation patterns.