Lagrange Programming Neural Network Approach for Frequency Diverse Array Beampattern Synthesis

Abstract

Different from traditional phased array radar, frequency diverse array (FDA) radar uses a small frequency increment across the array elements; thus, the beampattern of FDA can be performed in both range and angle domains. Nevertheless, the transmit beampattern of conventional FDA is coupled in range and angle domains, which is not suitable for target detection. In this paper, we propose a multi-carrier FDA framework to generate the range-angle-decoupled beampattern. To focus the transmit energy at the desired position and suppress the interference at the undesired position, we then propose a beampattern synthesis approach based on Lagrange programming neural network, which can be implemented by hardware easily. Numerical simulation results demonstrate the effectiveness of the proposed method.