The restricted isometry property of observation matrix in near-field measurements is unknown using random under-sampling strategy in compressed sensing based methods

which has a negative influence on the probability of success rate of diagnosis when adopting 1 norm minimization. In order to overcome this limitation

a hybrid diagnosis algorithm using random perturbation-non convex optimization for identification of impaired sensors in conformal arrays with near-field measurements is investigated. Differential array composed of healthy array and damaged array is constructed in the case of the sparsity of the number of failed elements. Then the near-field data are acquired. Finally

accurate diagnosis with high probability is achieved by recovering the sparse excitation of differential array utilizing proposed algorithm. Numerical simulation results demonstrate that the proposed method avoids the adverse impact on the performance of diagnosis arising from the absence of apriori information on RIP of observation matrix

overcomes the problem of local minima associated to the non-convex norm

and therefore improves the probability of success rate of diagnosis effectively.