Comparative Simulation Analysis of CAPON and MUSIC Frequency Spectrum Estimation Based on Array Antennas

Through comparative simulation analysis of CAPON and MUSIC frequency spectrum estimation methods based on array antennas, we can gain deep insights into the advantages and limitations of both approaches. CAPON (Capon Beamforming) is a classical adaptive beamforming algorithm that performs spatial spectrum estimation on received signals to achieve signal source localization and separation. In code implementation, this typically involves calculating the covariance matrix of received signals and applying minimum variance distortionless response (MVDR) criteria. MUSIC (Multiple Signal Classification) is a high-resolution spectrum estimation algorithm that utilizes eigenvalue decomposition of signals received by array antennas to achieve accurate localization of multiple signal sources. The algorithm implementation involves decomposing the covariance matrix into signal and noise subspaces, then searching for spectral peaks through orthogonal subspace comparisons. By conducting comparative analysis of CAPON and MUSIC methods, we can better understand their application scenarios and performance characteristics in spectrum estimation. CAPON generally provides better performance in scenarios with coherent signals but requires accurate covariance matrix estimation, while MUSIC offers superior resolution for uncorrelated signals but is sensitive to model mismatches. This comparative study holds significant importance for research and applications in array antenna-based wireless communication systems, providing guidance for algorithm selection and parameter optimization in practical implementations. Simulation code typically involves array geometry configuration, signal model generation, covariance matrix computation, and spectrum peak detection algorithms.