Two Novel Two-Stage Direction of Arrival Estimation Algorithms for Two-Dimensional Mixed Noncircular and Circular...

Area of Science:

• Signal Processing
• Array Signal Processing
• Electromagnetics

Background:

• Accurate two-dimensional direction-of-arrival (2D DOA) estimation is crucial for various applications, including radar, sonar, and wireless communications.
• Existing methods often struggle with mixed noncircular and circular signals and limited angular resolution.
• The need for advanced algorithms that can handle complex signal environments and improve parameter estimation is evident.

Purpose of the Study:

• To propose two novel methods, the two-stage direction-of-arrival matrix (TSDOAM) and two-stage rank reduction (TSRARE), for 2D DOA estimation.
• To enhance the estimation performance for mixed noncircular and circular signals.
• To improve the maximum detectable angle parameters and the number of detectable signals compared to existing techniques.

Main Methods:

• The TSDOAM method utilizes a two-stage approach based on direction-of-arrival matrix properties.
• The TSRARE method employs a two-stage rank reduction technique, fully leveraging the aperture of two parallel uniform linear arrays (ULAs).
• Both methods exploit signal circularity and DOA differences for improved estimation.

Main Results:

• The TSDOAM and TSRARE methods achieve maximum detectable 2D angle parameters twice that of existing methods.
• The TSRARE method demonstrates a superior ability to detect more incident signals by fully utilizing the array aperture.
• Simulations confirm that the proposed methods offer excellent performance in terms of signal-to-noise ratio (SNR) and snapshots, especially for small angle separations.

Conclusions:

• The TSDOAM and TSRARE methods represent significant advancements in 2D DOA estimation for mixed signal environments.
• These novel techniques offer enhanced angular resolution and increased signal detection capabilities.
• The proposed algorithms provide robust performance, particularly under low SNR and limited snapshot conditions.