Joint DOA Estimation and Array Calibration Using Multiple Parametric Dictionary Learning
This work addresses a domain-specific problem in signal processing for improving DOA estimation accuracy in imperfect sensor arrays, representing an incremental advancement.
The paper tackles robust direction of arrival (DOA) estimation in the presence of array imperfections like gain-phase errors and mutual coupling, proposing a multiple parametric dictionary learning algorithm that jointly solves DOA estimation and array calibration, with simulation results demonstrating its efficiency under off-grid and imperfection conditions.
This letter proposes a multiple parametric dictionary learning algorithm for direction of arrival (DOA) estimation in presence of array gain-phase error and mutual coupling. It jointly solves both the DOA estimation and array imperfection problems to yield a robust DOA estimation in presence of array imperfection errors and off-grid. In the proposed method, a multiple parametric dictionary learning-based algorithm with an steepest-descent iteration is used for learning the parametric perturbation matrices and the steering matrix simultaneously. It also exploits the multiple snapshots information to enhance the performance of DOA estimation. Simulation results show the efficiency of the proposed algorithm when both off-grid problem and array imperfection exist.