| With the rapid development of communication technology in today’s era,antennas play an important role in daily life and military activities,and array antennas have become an important part of it with their excellent performance.However,once the array antenna has a faulty array element,it will affect the normal operation of the array antenna,so it is necessary to locate the faulty array element in the array antenna.For the diagnosis of the array antenna fault,the radiation data is usually sampled in the far field and the true excitation coefficient of the array element is obtained by inverse calculation,so as to locate the faulty array element.The existing classical algorithms can solve such problems,but they do not take into account the mutual coupling between the elements,and the traditional methods require a high sampling rate.Therefore,the accurate diagnosis of faulty arrays with unknown mutual coupling using a low sampling rate becomes the goal of the research.The main research work in this paper is as follows:1.Starting from the physical properties of the array antenna,the far-field radiation model of the array antenna is established,and the linear equations containing the sparse unknowns are obtained by using the sparse characteristics of the faulty array elements,and the sparse unknowns are solved by using the Compressive Sensing(CS)theory.The Orthogonal Matching Pursuit(OMP)algorithm,FOCal Underdetermined System Solver(FOCUSS),and Bayesian Compressive Sensing(BCS)algorithm are used,respectively.Finally,numerical simulation experiments are designed to diagnose the uniform linear array antenna and uniform planar array antenna,respectively,to verify the feasibility and performance of the proposed diagnostic methods.2.For linear array antennas with unknown mutual coupling,a diagnosis method based on Bernoulli Gaussian(BG)a priori Bayesian learning is proposed.Firstly,a diagnostic model is constructed and the mutual coupling matrix is estimated based on the mutual coupling characteristics of the linear array;secondly,the sparse excitation is reconstructed and the BG prior is constructed using the binary structure of the array element excitation in the faulty array,and then the parameters are estimated using the Expectation-Maximization(EM)algorithm,while the Generalized Approximate Message Passing(GAMP)is used to reduce the computational complexity of the Finally,simulation experiments are designed to verify the accuracy and effectiveness of the proposed diagnostic method by comparing it with other traditional algorithms and analyzing the diagnostic effect under different signal-to-noise ratios and different sampling rates.3.For planar array antennas with unknown mutual coupling,a diagnostic method based on pattern-coupled hierarchy(PC)a priori Bayesian learning is proposed.Firstly,the sparse model is constructed by using the mutual coupling property of the planar array,and the mutual coupling matrix is estimated by the symmetric Toeplitz structure of the sub-array;secondly,the sparse model is reconstructed and solved,and the PC prior is constructed according to the block sparse structure of the faulty array elements in the planar array,and the parameters are estimated by the EM method;the mutual coupling matrix and the excitation vector are updated iteratively to finally realize the fault diagnosis of the planar array;Finally,the proposed diagnosis method is verified by simulation experiments and compared with other algorithms in different scenarios,and the results show that the proposed algorithm can effectively and accurately achieve the location of the faulty array elements. |
