Construction of Projection Matrices Based on Non-Uniform Sampling Distribution for AoA Estimation

• Published in: IEEE access Vol. 8; pp. 98369 - 98382
• Main Authors: Al-Sadoon, Mohammed Abdullah Ghali, Mosleh, Mahmood F., Ali, Nazar T., Migdadi, Hassan S., Abd-Alhameed, Raed A., Excell, Peter S.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Angle of arrival; Antenna array; Antenna arrays; AoA estimation; Complexity theory; Computer simulation; Correlation; Correlation analysis; Covariance; Covariance matrices; covariance matrix; Cramer-Rao bounds; Criteria; DOFs; Eigenvalues; Eigenvectors; Estimation; Lower bounds; Methodology; Navigation; non-uniform sampling; Projection; projection matrix; Sampling methods; Sensors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2993908

A Non-Uniform Sampling (NUS) methodology is presented, which improves the Angle of Arrival (AoA) estimation accuracy. The proposed sampling methodology is applied to extract the collected data efficiently and to reduce the possible dependency within rows/columns of the Covariance / Correlation Matrix (CM). The new sampled matrix approach is utilized to form such a projection matrix in order to provide better resolution of angles of arrival for closely incident signals on the antenna array receiver and to increase Degrees of Freedom (DOFs) compared to the classical criterion. The new direction-finding method based on the NUS methodology is called a Non-Uniform Projection Matrix (NUPM). A theoretical analysis is presented to demonstrate the advantage of the NUS methodology in terms of the obtained energy of the eigenvalues that are associated with the signal eigenvectors. It is proved that the proposed matrix sampling approach can provide better estimation resolution and detection of higher numbers of angles of arrival compared to the classical sampling method, without increasing array aperture size and the complexity of computations. A reduced-dimension process is applied to the NUPM in order to decrease the computational burden at the grid-scanning step. A computer simulation, including many scenarios, is implemented to justify the expected improvements of the NUS methodology compared to the classical approach. It is found that the new sampling distribution enhances noise immunity and increases DOFs compared to the conventional criterion. The performance of the NUPM method is compared with several AoA methods, including the Cramer-Rao Lower Bound (CRLB), and the obtained results verify the effectiveness and robustness of the proposed NUPM technique.