Direction of Arrival (DOA) Estimation with Enhanced MUSIC Algorithm Implementation

In signal processing, Direction of Arrival (DOA) estimation constitutes a fundamental problem focused on determining spatial positions of multiple signals of interest within a specified spatial region - specifically the azimuth angles at which signals arrive at array reference elements. The classical super-resolution DOA estimation method is the renowned Multiple Signal Classification (MUSIC) algorithm. This technical report presents implementation steps for an enhanced MUSIC methodology. We begin by explaining the working principle of the standard MUSIC algorithm, which involves computing the signal covariance matrix from array receiver data, performing eigenvalue decomposition to separate signal and noise subspaces, and constructing the MUSIC spatial spectrum using orthogonality between steering vectors and noise eigenvectors. The limitations of conventional MUSIC regarding computational complexity and resolution thresholds under low SNR conditions will be discussed. The proposed improvements incorporate advanced covariance matrix estimation techniques and optimized subspace tracking algorithms. Implementation details include code structures for adaptive threshold setting in peak detection and methods for handling coherent signals through spatial smoothing preprocessing. We present comprehensive simulation results comparing performance metrics (resolution probability, RMSE) between standard and enhanced MUSIC under varying SNR conditions and source separations. The report concludes with performance analysis and practical considerations for real-time implementation. Through studying this report, readers will gain deeper understanding of DOA estimation methodologies and specific enhancement strategies for MUSIC algorithms, including MATLAB/Python code snippets for key components like signal subspace identification and spatial spectrum computation.