Robust adaptive beamforming via improved worst-case performance optimization algorithm based on FDA-MIMO

Owing to the property of range-angle-dependent beampattern, frequency diverse array (FDA) has promising application potentials in radar field. Furthermore, Combined with multiple-input multiple-output (MIMO) scheme, the transmit waveform can be separated at the receiver, which can increase the flexibility of signal processing. In this paper, we consider robust adaptive beamforming (RAB) design for FDA-MIMO system, and propose an improved worst-case performance optimization (IWCPO) algorithm which takes advantages of the idea of eigenspace to reconstruct the covariance matrix, and obtains the optimal solution of the beamformer through convex optimization theory. The simulation results show that contrary to the WCPO algorithm, the proposed algorithm offers a significant performance improvement in case of large range mismatch and the presence of the desired signal component in the training data. Compared with several previous RAB algorithms, the performance of the proposed algorithm shows superior performance across a wide range of input signal to noise ratios (SNR).