Persymmetric design of jointly detection and bearing estimation for a 2D array radar in training demanding scenarios

Abstract

The problem of joint detection and bearing estimation for a two-dimensional (2D) array radar in training demanding scenarios is addressed. Regardless of the cause of the angle deviation, we model the 2D steering vector as a fully incremental form. Thus, the 2D array requires the optimization of two target cosine offsets. To relax the requirement of sufficient training data, we incorporate persymmetric structure in the design of the receiver. Unlike traditional joint optimization, the persymmetric structure brings a more complex multidimensional matrix form, making detection optimization more difficult. Then, we transform the optimization problem into a fractional-order programming problem and solve it by Dinkelbach algorithm. Finally, numerical results verify the superiority of the proposed method over conventional methods in insufficient training scenarios. Also, the proposed method is more robust.