An Enhanced Direct Position Determination of Mixed Circular and Non-Circular Sources Using Moving Virtual...

Area of Science:

• Signal Processing
• Array Signal Processing
• Array Geometry

Background:

• Existing moving single-station direct position determination (DPD) algorithms struggle with sparse array aperture utilization and mixed signal types.
• Challenges include incomplete data and insufficient handling of both circular and non-circular signals.

Purpose of the Study:

• To propose an enhanced gridless DPD algorithm for improved localization of multiple mixed circular and non-circular sources.
• To address limitations in existing algorithms regarding data utilization and signal type handling.

Main Methods:

• Constructing a non-zero unconjugated covariance matrix from non-circular signal components to expand data dimensionality.
• Employing a gridless approach to fill coarray voids, enhancing localization performance.
• Applying unitary transformations to convert complex operations into real operations, reducing computational complexity.

Main Results:

• The proposed algorithm significantly improves localization performance by effectively utilizing sparse array data.
• Demonstrated advantages in array degrees of freedom and localization accuracy compared to existing methods.
• Reduced computational complexity through unitary transformation techniques.

Conclusions:

• The enhanced gridless DPD algorithm offers superior performance for moving single-station localization.
• The method effectively addresses challenges associated with sparse arrays and mixed signal types.
• The algorithm provides a more accurate and computationally efficient solution for direct position determination.