Mainlobe Interference Suppression Method Based on Virtual Array Expansion with Non-Circular Signal for Array Radar

‍ ‍In the electronic countermeasures’ environment， complex and variable RF interference incident the phased array radar. Adaptive beamforming methods can suppress interference by the weighted summation of each array element to form a mainlobe in the direction of the desired signal and a nulling in the direction of interferences， which improves the detection ability of the array radar. However， when the interference falls into the mainlobe， the conventional beamforming method suffers from beampattern distortion， mainlobe position deviation， sidelobe level elevation， etc.， which will cause the loss of the desired signal， resulting in declining the output signal to interference plus noise ratio （SINR）. To solve this problem， a model of the loss of SINR in the presence of mainlobe interference is established， and the relationship between output SINR and array aperture is obtained. Based on theoretical analysis， a mainlobe interference suppression method based on virtual array expansion with noncircular signal for array radar is proposed in this paper. First， based on the characteristic of a non-circular signal， a virtual expanded array aperture is designed using the conjugate symmetry of the original array without adding actual elements. An elements layout optimization method based on a genetic algorithm is then proposed to change the roll-of characteristics of the pattern to optimize the array pattern. Finally， the minimum variance distortion-less response algorithm is employed to suppress the mainlobe interference in the virtual expanded array and the optimized array， which can reduce the desired signal loss caused by mainlobe interference and improve the output SINR. Numerical simulations show that the proposed method is superior to existing methods in jamming suppression under different array elements and different non-circular signals. The experimental results show that the proposed method is effective and has potential application in fields such as early warning and target detection.