190 research outputs found
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
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.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052
On the resolution of the LASSO-based DOA estimation method
This paper investigates the consistency of the LASSO-based DOA estimation of the narrow-band signals in infinitely high SNR. Such a method provides a robust and accurate approximation of the Maximum Likelihood estimation. However, as we show, unlike the standard techniques such as subspace methods the LASSO-based estimation is generally not consistent in high SNRs. In return, considering the true DOA's, we show that the method is consistent for certain configuration of the sources. This approach leads us to relate such a conditional consistency to the resolution concept. We next give a condition to verify the consistency of a given set of directions and simplify it to a computationally fast equivalent algorithm. The results show that the resolution in infinitely high SNR case for m sensors decreases by speed 1 over m
URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming
The computational complexity of the conventional adaptive beamformer is
relatively large, and the performance degrades significantly due to both the
model mismatch errors and the unwanted signals in received data. In this paper,
an efficient unwanted signal removal and Gauss-Legendre quadrature
(URGLQ)-based covariance matrix reconstruction method is proposed. Different
from the prior covariance matrix reconstruction methods, a projection matrix is
constructed to remove the unwanted signal from the received data, which
improves the reconstruction accuracy of the covariance matrix. Considering that
the computational complexity of most matrix reconstruction algorithms are
relatively large due to the integral operation, we proposed a Gauss-Legendre
quadrature-based method to approximate the integral operation while maintaining
the accuracy. Moreover, to improve the robustness of the beamformer, the
mismatch in the desired steering vector is corrected by maximizing the output
power of the beamformer under a constraint that the corrected steering vector
cannot converge to any interference steering vector. Simulation results and
prototype experiment demonstrate that the performance of the proposed
beamformer outperforms the compared methods and is much closer to the optimal
beamformer in different scenarios.Comment: 11 pages, 16 figure
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