Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications

Area of Science:

• Electrical Engineering
• Ocean Engineering
• Signal Processing

Background:

• Sparse arrays offer cost savings and maintain resolution by reducing element count.
• Practical deployment faces challenges like grating lobe suppression, peak side-lobe level reduction (PSLL), and element placement constraints.

Purpose of the Study:

• To optimize element locations for sparse planar arrays in smart ocean applications.
• To address challenges of grating lobe suppression and PSLL reduction under spatial constraints.
• To evaluate the robustness of optimized arrays against mis-calibration.

Main Methods:

• Simulated annealing algorithm applied for element location optimization.
• Minimization of the sum of peak side-lobe levels (PSLL) as the objective function.
• Consideration of minimum element spacing and geographic constraints during optimization.

Main Results:

• Optimized sparse planar array designs achieved significant grating lobe suppression and PSLL reduction.
• The simulated annealing approach effectively handled spatial and geographic deployment constraints.
• The optimized array demonstrated robustness against element mis-calibration in numerical simulations.

Conclusions:

• The proposed simulated annealing method provides an effective solution for optimizing sparse planar array deployment in smart ocean applications.
• The optimized arrays maintain performance while adhering to practical deployment limitations.
• The approach enhances the reliability of sparse arrays in real-world ocean environments.