Statistics on noise covariance matrix for covariance fitting-based compressive sensing direction-of-arrival...

Area of Science:

• Signal Processing
• Array Signal Processing
• Electromagnetics

Background:

• Direction-of-arrival (DOA) estimation is crucial for applications like radar and sonar.
• Existing covariance fitting methods often require a large number of snapshots or predefined regularization parameters.
• Spatially sparse signal models offer potential for improved DOA estimation.

Purpose of the Study:

• To develop a covariance fitting algorithm for DOA estimation of multiple incident signals.
• To present a strategy for determining the regularization constant for a general number of snapshots.
• To leverage the spatial sparsity of incident signals for enhanced estimation.

Main Methods:

• A covariance fitting algorithm is employed for DOA estimation.
• The strategy for determining the regularization constant exploits the norm of the noise covariance matrix.
• Numerical simulations are used to validate the proposed algorithm.

Main Results:

• The proposed strategy effectively determines the regularization constant for covariance fitting.
• The algorithm demonstrates accurate DOA estimation for spatially sparse signals.
• Validation through numerical simulations confirms the algorithm's performance.

Conclusions:

• A novel covariance fitting strategy for DOA estimation is presented.
• The method provides a robust way to determine regularization constants, applicable to a general number of snapshots.
• The algorithm is effective for estimating directions-of-arrival in scenarios with spatially sparse signals.