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.

中文翻译：

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训练要求较高的场景下二维阵列雷达联合检测和方位估计的非对称设计

解决了训练要求较高的场景中二维（2D）阵列雷达的联合检测和方位估计问题。无论角度偏差的原因如何，我们都将 2D 转向矢量建模为完全增量形式。因此，二维阵列需要优化两个目标余弦偏移。为了放宽对足够训练数据的要求，我们在接收器的设计中采用了非对称结构。与传统的联合优化不同，非对称结构带来了更复杂的多维矩阵形式，使得检测优化更加困难。然后，我们将优化问题转化为分数阶规划问题，并通过Dinkelbach算法进行求解。最后，数值结果验证了该方法在训练不足的场景下相对于传统方法的优越性。此外，所提出的方法更加稳健。