Joint Estimation of Azimuth and Elevation Angles using Uniform Circular Arrays

This text discusses a joint estimation method for azimuth and elevation angles based on uniform circular arrays, utilizing the MUSIC (Multiple Signal Classification) algorithm and MNM (Minimum Norm Method) algorithm for estimation. These algorithms are fundamental techniques in signal processing and wireless communications that enhance the accuracy of direction of arrival (DOA) estimation, thereby improving wireless signal identification capabilities. The MUSIC algorithm typically involves computing the covariance matrix of received signals, performing eigenvalue decomposition to separate signal and noise subspaces, and then searching for peaks in the spatial spectrum. The MNM algorithm operates by finding the solution with minimum norm that satisfies the signal subspace constraints. Additionally, other circular array-based estimation methods such as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) and ROOT-MUSIC algorithms can be implemented to further improve estimation precision. ESPRIT leverages the rotational invariance property of array structures without requiring exhaustive spectral searches, while ROOT-MUSIC transforms the spectral search into a polynomial rooting problem for computational efficiency. Understanding and mastering these algorithms is crucial for professionals working in signal processing and communication fields, as they form the basis for advanced DOA estimation systems in radar, sonar, and wireless network applications.