Direction of Arrival Estimation of Generalized Nested Array via Difference-Sum Co-Array

Area of Science:

• Array Signal Processing
• Electromagnetics
• Statistical Signal Processing

Background:

• Difference Co-Array (DCA) methods offer limited enhancement in degrees of freedom (DOFs) for Direction of Arrival (DOA) estimation.
• Existing sparse array configurations face challenges in maximizing virtual aperture and estimation performance.

Purpose of the Study:

• To propose a novel Generalized Nested Array via Difference-Sum Co-Array (GNA-DSCA) configuration for improved DOA estimation.
• To systematically analyze the properties of the GNA-DSCA, including its DOFs and virtual aperture.
• To enhance the accuracy of DOA estimation by addressing performance degradation caused by non-uniform virtual arrays.

Main Methods:

• Construction of the Difference-Sum Co-Array (DSCA) model utilizing both temporal and spatial information.
• Systematic analysis of the Difference Co-Array (DCA) and Sum Co-Array (SCA) for the Generalized Nested Array (GNA).
• Derivation of closed-form expressions for DOFs under determined dilation factors.
• Application of an Atomic Norm Minimization (ANM) based hole-filling strategy.

Main Results:

• The GNA-DSCA configuration provides a more flexible design and significantly more DOFs compared to the GNA-DCA.
• Larger dilation factors in GNA-DSCA result in a substantially wider virtual aperture, outperforming existing DSCA-based sparse arrays.
• The ANM hole-filling strategy effectively mitigates performance degradation from non-uniform virtual arrays, enabling accurate DOA estimation.

Conclusions:

• The proposed GNA-DSCA configuration offers superior virtual array properties and enhanced DOA estimation performance.
• The GNA-DSCA presents a more attractive and flexible alternative for high-resolution DOA estimation applications.
• The combination of GNA-DSCA and ANM hole-filling provides a robust solution for accurate direction finding.