Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
This is an incremental improvement for DOA estimation in signal processing applications.
The authors tackled direction-of-arrival (DOA) estimation by proposing a subspace-based algorithm that iteratively reduces disturbances in the covariance matrix and incorporates online prior knowledge, achieving improvements in simulations with closely-spaced sources (both uncorrelated and correlated).
In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.