A Novel Sparse Framework for Angle and Frequency Estimation.

Area of Science:

• Signal Processing
• Array Signal Processing

Background:

• Traditional joint angle and frequency estimation (JAFE) relies on the Nyquist sampling criterion, leading to high hardware costs and computational burdens.
• Existing uniform sampling architectures face limitations in achieving unambiguous parameter estimation without significant complexity.

Purpose of the Study:

• To propose a novel sparsity-aware framework to reduce the complexity of joint angle and frequency estimation (JAFE).
• To introduce an improved rotational invariance estimator offering closed-form solutions for angle and frequency estimation.

Main Methods:

• A sparsity-aware framework utilizing space-time coprime samplers for narrow-band signals.
• An improved rotational invariance estimator for simultaneous angle and frequency estimation.

Main Results:

• The proposed methodology achieves a larger spatial/temporal aperture than uniform sampling architectures, resulting in more accurate JAFE.
• The framework attains nearly the same computational complexity as current rotational invariance approaches.
• Numerical results validate the theoretical advantages of the proposed methodology.

Conclusions:

• The novel sparsity-aware framework effectively reduces complexity and enhances accuracy in joint angle and frequency estimation.
• Coprime sampling combined with an improved rotational invariance estimator offers a superior alternative to traditional JAFE methods.