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Multi-step Knowledge-Aided Iterative Conjugate Gradient Algorithms for DOA Estimation

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Abstract

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.

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  1. Pontifical Catholic University of Rio de Janeiro - CETUC, Rio de Janeiro, Brazil

    Silvio F. B. Pinto & Rodrigo C. de Lamare

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  1. Silvio F. B. Pinto
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  2. Rodrigo C. de Lamare
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Correspondence to Silvio F. B. Pinto.

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Pinto, S.F.B., de Lamare, R.C. Multi-step Knowledge-Aided Iterative Conjugate Gradient Algorithms for DOA Estimation. Circuits Syst Signal Process 38, 3841–3859 (2019). https://doi.org/10.1007/s00034-019-01033-0

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