4 research outputs found
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Direction of Arrival Estimation and Sensor Array Error Calibration Based on Blind Signal Separation
We consider estimating the direction of arrival (DOA) in the presence of sensor array error. In the proposed method, a blind signal separation method, the Joint Approximation and Diagonalization of Eigenmatrices (JADE) algorithm, is implemented to separate the signal vector and the mixing matrix consisting of the array manifold matrix and the sensor array error matrix. Based on a new mixing matrix and the reconstruction of the array output vector of each individual signal, we propose a novel DOA estimation method and sensor array error calibration procedure. This method is independent of array phase errors and performs well against difference of SNR of signals. Numerical simulations verify the effectiveness of the proposed method
Parametric array calibration
The subject of this thesis is the development of parametric methods for the calibration of array
shape errors. Two physical scenarios are considered, the online calibration (self-calibration)
using far-field sources and the offline calibration using near-field sources. The maximum
likelihood (ML) estimators are employed to estimate the errors. However, the well-known
computational complexity in objective function optimization for the ML estimators demands
effective and efficient optimization algorithms.
A novel space-alternating generalized expectation-maximization (SAGE)-based algorithm is
developed to optimize the objective function of the conditional maximum likelihood (CML)
estimator for the far-field online calibration. Through data augmentation, joint direction of
arrival (DOA) estimation and array calibration can be carried out by a computationally simple
search procedure. Numerical experiments show that the proposed method outperforms the existing
method for closely located signal sources and is robust to large shape errors. In addition,
the accuracy of the proposed procedure attains the Cram´er-Rao bound (CRB).
A global optimization algorithm, particle swarm optimization (PSO) is employed to optimize
the objective function of the unconditional maximum likelihood (UML) estimator for the farfield
online calibration and the near-field offline calibration. A new technique, decaying diagonal
loading (DDL) is proposed to enhance the performance of PSO at high signal-to-noise
ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the
global optimum of the UML objective function is more prominent at lower SNR. Numerical
simulations demonstrate that the UML estimator optimized by PSO with DDL is optimally accurate,
robust to large shape errors, and free of the initialization problem. In addition, the DDL
technique is applicable to a wide range of array processing problems where the UML estimator
is employed and can be coupled with different global optimization algorithms