Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling

Area of Science:

• Signal Processing
• Array Signal Processing
• Electromagnetics

Background:

• Direction-of-arrival (DOA) estimation is crucial for sensor array applications.
• Unknown mutual coupling between sensors significantly degrades traditional DOA estimation performance.
• Existing off-grid DOA estimation methods using sparse recovery are limited, especially under mutual coupling.

Purpose of the Study:

• To investigate off-grid DOA estimation in sensor arrays with unknown mutual coupling.
• To develop a novel and robust DOA estimation algorithm for challenging environments.
• To enhance the accuracy and efficiency of direction finding techniques.

Main Methods:

• A reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is proposed.
• An undisturbed array output is formed using a selection matrix to mitigate mutual coupling effects.
• A refined off-grid SpSF model integrates an off-grid error term, and a weighted matrix enhances sparsity.

Main Results:

• The Re-OGSpSF method effectively removes unknown mutual coupling effects.
• High-precision continuous DOA estimation is achieved through off-grid representation and reweighted strategies.
• The proposed method demonstrates superior performance and efficiency compared to existing techniques in simulations.

Conclusions:

• The developed Re-OGSpSF approach provides a robust solution for off-grid DOA estimation under unknown mutual coupling.
• The method offers significant advantages for real-time direction finding applications.
• Simulation results confirm the effectiveness and superiority of the proposed technique.