1 research outputs found
Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithms
In this work, we present direction-of-arrival (DoA) estimation algorithms
based on the Krylov subspace that effectively exploit prior knowledge of the
signals that impinge on a sensor array. The proposed multi-step knowledge-aided
iterative conjugate gradient (CG) (MS-KAI-CG) algorithms perform subtraction of
the unwanted terms found in the estimated covariance matrix of the sensor data.
Furthermore, we develop a version of MS-KAI-CG equipped with forward-backward
averaging, called MS-KAI-CG-FB, which is appropriate for scenarios with
correlated signals. Unlike current knowledge-aided methods, which take
advantage of known DoAs to enhance the estimation of the covariance matrix of
the input data, the MS-KAI-CG algorithms take advantage of the knowledge of the
structure of the forward-backward smoothed covariance matrix and its
disturbance terms. Simulations with both uncorrelated and correlated signals
show that the MS-KAI-CG algorithms outperform existing techniques.Comment: 7 figures, 11 page