Implementation of a DOA Estimation Algorithm Using Subspace Classification

In this article, we discuss a Direction of Arrival (DOA) estimation algorithm that leverages subspace classification techniques, particularly the Rotation Invariance Subspace Algorithm (ESPRIT). This algorithm transforms received signals into subspace representations through covariance matrix computations and eigenvalue decomposition. The core implementation involves constructing a signal subspace from the eigenvectors corresponding to the largest eigenvalues, then exploiting the rotational invariance property between subarrays to estimate signal directions. A key advantage lies in its mathematical formulation that inherently suppresses noise and interference by separating signal and noise subspaces. The algorithm effectively handles multiple coherent signals through techniques like spatial smoothing, making it suitable for scenarios with multipath propagation. Implementation typically involves MATLAB or Python code utilizing matrix operations (e.g., numpy.linalg.svd() for singular value decomposition) and array geometry calculations. Practical applications span radar systems (beamforming direction finding), acoustic signal processing (microphone array source localization), and wireless communications (smart antenna systems), demonstrating significant real-world viability across these domains.