An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays.

Area of Science:

• Signal Processing
• Array Signal Processing

Background:

• Traditional angle estimation methods for 2D co-prime planar arrays rely on peak searching or sparse recovery.
• Peak searching involves high computational complexity, while sparse recovery faces challenges in regularization parameter selection.

Purpose of the Study:

• To propose an improved trilinear model-based method for angle estimation in co-prime planar arrays.
• To reduce computational complexity and eliminate the need for parameter selection in angle estimation.

Main Methods:

• Utilizes trilinear decomposition, specifically parallel factor analysis, for angle estimation.
• Exploits the virtual array concept via cross-correlation matrices.
• Employs a matrix reconstruction method using the Khatri-Rao product to address rank deficiency.

Main Results:

• The proposed method achieves high estimation accuracy with reduced computational complexity.
• It effectively handles matrix rank deficiency in virtual array conditions.
• Demonstrates successful angle estimation using a limited number of sensors.

Conclusions:

• The improved trilinear model offers a computationally efficient and accurate solution for angle estimation in co-prime planar arrays.
• The method overcomes limitations of existing techniques, enabling practical applications with fewer sensors.