Abstract
The use of ℓ p ( 0 < p < 1 ) norm minimization will improve array diagnosis performance provided that the issue of local minima associated to its non-convex nature is properly handled. In order to overcome this deficiency, a hybrid method using random perturbation and non-convex optimization is investigated in this paper. Although it acquires a higher computational time, the trade-off between an accurate diagnosis and the computational burden appears to be acceptable. Theoretical analysis and simulation results demonstrate that the proposed method overcomes this disadvantage effectively and achieves better performance compared to the standard ℓ 1 norm minimization with a smaller number of far-field measurements, suggesting that the proposed method can be used to improve the performance of array diagnosis.
Highlights
Array antennas are widely used in radar, remote sensing and mobile and satellite communications, etc
The Compressed Sensing/Sparse Recovery (CS/SR) based approaches have been proposed in the framework of array diagnosis [10,11,12,13]
The aim of this paper is to increase the probability of success rate of diagnosis with a smaller number of far-field measurements using a hybrid method of random perturbation and non-convex optimization, to overcome the issue of local minima associated to the intrinsic non-convex nature ofp (0 < p < 1) norm minimization
Summary
Array antennas are widely used in radar, remote sensing and mobile and satellite communications, etc. In [16] a fast diagnosis method of antenna arrays using a small number of far-field measurements with simulated and experimental data is addressed. The aim of this paper is to increase the probability of success rate of diagnosis with a smaller number of far-field measurements using a hybrid method of random perturbation and non-convex optimization, to overcome the issue of local minima associated to the intrinsic non-convex nature ofp (0 < p < 1) norm minimization. Lots of numerical experiments are carried out and the results have vividly confirmed that the proposed method overcomes this problem effectively, and obtains a stronger promotion of sparsification of the solutions compared to the standard norm minimization with an acceptable increase of calculated amount
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