Enhanced DOA Estimation Algorithm Using MUSIC Method

The MUSIC algorithm (Multiple Signal Classification) represents a classic DOA estimation technique widely employed in radar systems, sonar applications, and wireless communications. This method estimates signal arrival directions by leveraging the orthogonality between signal and noise subspaces, delivering superior resolution and estimation accuracy. Although traditional MUSIC demonstrates excellent performance, it faces several practical limitations. Under low signal-to-noise ratio (SNR) conditions, algorithm performance degrades significantly. When strong correlation exists between signal sources, the method may fail entirely. Additionally, high computational complexity presents substantial challenges for real-time implementation. Researchers have developed multiple enhancement strategies to address these issues. A common approach integrates complementary signal processing techniques like sparse representation or compressed sensing to improve robustness in low-SNR environments. Code implementation typically involves modifying the covariance matrix calculation and incorporating L1-norm optimization constraints. Another improvement focuses on optimizing subspace decomposition through weighted covariance matrices or enhanced eigenvalue decomposition methods, which can be implemented using MATLAB's svd() function with customized weighting schemes. Several studies concentrate on reducing computational complexity for real-time applications. Fast subspace tracking algorithms and approximation techniques substantially decrease computational burden while maintaining estimation performance. Python implementations often employ NumPy's efficient linear algebra routines and iterative methods for covariance matrix updates. These enhancement methods offer distinct advantages, with optimal selection dependent on specific application scenarios and performance requirements. Practical implementations typically involve trade-offs between computational complexity, estimation accuracy, and robustness, requiring careful parameter tuning in the eigenvalue threshold setting and subspace dimension selection phases.