凸集优化稳健自适应DBF 的在线算法

一种基于凸集优化稳健DBF 的在线算法，以克服平稳和非平稳误差引起的阵列性能下降。算法利用球体约束和 权值约束，建立凸集最优目标函数，采用最速下降法确定对角负载因子，并迭代求解最优权矢量。仿真结果表明，与传 统的capon 算法和固定对角负载值算法相比，基于凸集优化的迭代算法具有更大的阵列输出信干噪比，从而使阵列具有 更好的稳健性。

On-line Algorithm of Robust Adaptive DBF Based on Convex Optimization

A new on-line algorithm of robust adaptive digital beamforming is proposed, which could calibrate various stationary and nonstationary mismatches. The convex optimization function rises from the spherical constrain and weight vector model constrain. The method of steepest descent is taken to calculate the diagonal loading values and to implement the iteration. The number simulations show that the proposed algorithms could achieve better output signal to interfere and noise ratio than the standard Capon algorithm and classic diagonal loading algorithm. Then the new algorithms improve the robust performance of adaptive DBF.