Beamforming with semi-coprime arrays

Area of Science:

• Array Signal Processing
• Electromagnetics and Wave Propagation

Background:

• Sparse arrays are crucial for efficient sensor utilization in signal processing.
• Existing sparse arrays like coprime and nested arrays have limitations in sensor count and performance.
• Grating lobes and side lobe patterns are key challenges in array beampattern design.

Purpose of the Study:

• To introduce and analyze a semi-coprime array (SCA) for direction of arrival (DOA) estimation.
• To demonstrate the SCA's advantages over existing sparse array configurations.
• To highlight the SCA's potential for reducing sensor requirements and system complexity.

Main Methods:

• The SCA design interleaves two undersampled uniform linear arrays (ULAs) with a standard ULA.
• A beampattern free of grating lobes is achieved by minimizing the absolute values of conventional beampatterns.
• Performance is evaluated based on sensor count, side lobe levels, and DOA estimation capabilities.

Main Results:

• The SCA requires fewer sensors compared to coprime, nested, and minimum redundant arrays for a given aperture.
• The SCA exhibits superior side lobe patterns, enhancing signal masking and weak source detection.
• DOA estimation with the SCA demonstrates significant reductions in sensor count, cost, and complexity.

Conclusions:

• The semi-coprime array (SCA) presents a highly sparse and efficient solution for DOA estimation.
• SCA technology offers practical advantages in terms of sensor reduction and system cost-effectiveness.
• The SCA's improved beampattern characteristics make it suitable for diverse signal processing applications.