Robust adaptive beamforming against large DOA mismatch with linear phase and magnitude constraints for multiple-input-multiple-output radar

In this study, a robust adaptive beamformer against large direction-of-arrival (DOA) mismatch for multiple-input–multiple-output radar is proposed with linear phase and magnitude constraints on main lobe. First, the full-dimensional weight vector (WV) is expressed as the Kronecker product of the transmit and receive array WVs based on the WV separable principle. For the transmit array WV, the authors find an interesting property that the Fourier spectrum of its conjugate inverse arrangement is equal to its array response function within a phase factor. This property also exists in the receive array WV. Using this property, the phase response of the transmit and receive array, respectively, is set to be linear based on designing a finite impulse response filter. Then, a bi-quadratic cost function with respect to the transmit and receive WVs is established by only constraining the real magnitude response and it is effectively solved by the bi-iterative algorithm. The proposed beamformer has lower computational complexity and faster sample convergence rate, compared with the traditional magnitude response constraints beamformers with full degrees of freedom. Moreover, it can provide good robustness against large DOA mismatch. Numerical experiments are provided to demonstrate the effectiveness of the proposal.