A Hybrid Method for Array Diagnosis Using Random Perturbation-Convex Local Minimizer

The use of ℓ
_p
(0 <; p <; 1) norm minimization will improve the performance of array diagnosis provided that the issue of local minima associated to its non-convex nature is properly handled. In order to figure out this drawback, a hybrid diagnosis method of random perturbation-convex local minimizer is addressed in this paper. Theoretical analysis and numerical simulations indicate the effectiveness of the proposed method to overcome this deficiency and outperforms the standard ℓ
₁
norm minimization in terms of locating broken elements with a smaller number of far-field measurements. Although it suffers from a higher computational burden, the trade-off between an accurate diagnosis with high probability and the computational time appears to be acceptable, suggesting that the proposed method can be a suitable candidate used to improve the performance of array diagnosis.