信号子空间 Resources

Showing items tagged with "信号子空间"

The MUSIC algorithm is a subspace decomposition method that separates the observation space into signal and noise subspaces. From a geometric perspective, these two subspaces are orthogonal, where the signal subspace comprises eigenvectors corresponding to signals in the data covariance matrix, while the noise subspace contains eigenvectors associated with the smallest eigenvalues (noise variance). This implementation utilizes Python/NumPy routines for covariance matrix computation, eigenvalue decomposition via numpy.linalg.eig, and pseudospectrum construction through noise subspace vectors. The algorithm achieves high-resolution direction-of-arrival estimation compatible with arbitrary array geometries.

MATLAB 222 views Tagged
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The MUSIC algorithm separates signal and noise subspaces by eigen-decomposition of the received data covariance matrix (Rx). It constructs spatial scanning spectra by exploiting the orthogonality between signal steering vectors and noise subspace, then performs peak searching in the parameter domain for accurate signal parameter estimation. Implementation typically involves eigenvalue decomposition, subspace identification, and peak detection algorithms.

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This study analyzes the Incoherent Signal Subspace Method (ISM) for wideband source DOA estimation and applies a modified MUSIC algorithm with data matrix conjugate reconstruction to enhance resolution and enable coherent source detection. The Coherent Signal Subspace Method (CSM) is discussed with analysis of focusing matrices and frequency selection impacts, including criteria for optimal focusing matrix and frequency selection. For colored noise environments, a novel DOA estimation approach integrating propagator operator concepts with TCT focusing matrices is developed, enabling efficient noise covariance estimation directly from array signals without complex eigenvalue decomposition.

MATLAB 257 views Tagged
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