DOA Estimation for Coherent Distributed Sources Using Generalized MUSIC Algorithm

This article explores DOA (Direction of Arrival) estimation for coherent distributed signal sources using the generalized MUSIC algorithm. This methodology addresses the challenge of signal source localization in distributed antenna arrays. The implementation typically involves constructing a spatial covariance matrix from array received data, followed by eigenvalue decomposition to separate signal and noise subspaces. The generalized MUSIC algorithm extends the standard approach by incorporating distributed source characteristics through angular spread parameters.

We will detail the integration of the generalized MUSIC framework with coherent signal subspace methods (CSSM), which involves forward/backward spatial smoothing techniques to decorrelate coherent sources. The algorithm implementation includes steps for calculating the generalized steering vectors that account for distributed source geometry, and subsequently computing the spatial spectrum using the noise subspace eigenvectors.

The discussion covers both advantages and limitations of this approach, including its enhanced resolution for distributed sources compared to point source models, and computational complexity considerations. Potential improvements involve adaptive angular sector selection and integration with compressed sensing techniques for better performance in low SNR scenarios. Through comprehensive analysis of this topic, we gain deeper insights into DOA estimation challenges in signal processing and practical applications in radar, wireless communications, and acoustic source localization.

Key implementation aspects include: covariance matrix estimation using sample averaging, subspace decomposition via SVD or EVD, and spectrum peak search algorithms. The code structure typically involves functions for array manifold calculation, spatial smoothing preprocessing, and MUSIC spectrum computation with distributed source parameters.