ANN-Assisted Quasi-Time-Invariant Beamforming for Retrodirective Frequency Diverse Array

Due to the range-angle-dependent beampattern, frequency diverse array (FDA) has attracted extensive attention. However, the inherent time-varying and range-angle coupling problems of the FDA are still troublesome. In this article, a quasi-time-invariant focusing beamforming scheme for retrodirective (RD)-FDA is proposed to alleviate the above tough problems. Specifically, linear and semicircular RD-FDAs are explored to achieve automatic beam alignment of the desired target without phase weights. By establishing an artificial neural network (ANN) framework, frequency offsets can be optimized to match the radiation requirements. By minimizing the loss value of the network, the desired focusing pattern can be realized. Then, optimized frequency offsets are periodically inverted to limit the signal phase, which allows flexible control of the spatial variation range of the mainlobe beam. Simulation results show the effectiveness and superiority of the proposed scheme to achieve quasi-time-invariant beamforming.