基于改进平滑 l 0 范数的 DOA 估计算法

为提高现有基于压缩感知的 DOA(Direction of Arrival)估计算法估计精度, 提出一种基于改进平滑 l 0 范数 的 DOA 估计算法。 该算法在构造一个恰当的平滑连续函数后根据接收数据的初始解确定一个合适的递减{δ} 序列δ1 ,δ 2 ,…,δ J ], 并对每个δ 值, 采用最速下降法求解 l 0 范数逼近函数 Fδ (S)的最小值; 然后将该δ 值作 为下一次迭代的初始值, 并通过多次的迭代获得逼近函数的最小解, 即逼近的最小 l 0 范数。 同时通过仿真实 验对该算法进行了验证。 结果表明, 该算法在单快拍条件下即可对 DOA 进行有效估计, 与 OMP(Orthogonal Matching Pursuit)算法相比, 运算过程简单、 精度较高, 具有更好的估计性能。

DOA Estimation Algorithm Based on Improved Smoothed l0 Norm

In order to improve the estimation accuracy of the existing DOA (Direction of Arrival) estimation algorithms based on compressive sensing, a DOA estimation algorithm based on Improved Smoothed l 0 norm is proposed. Firstly, we construct a proper smooth continuous function and give a proper decreasing sequence of sets {δ} which is δ 1 ,δ 2 ,…,δ J ] based on the initial solution of receiving data to get the minimum value of Fδ (S) for every singleδ by using steepest descent method, secondly we take thisδ as the original value for the next iteration, and then we get the minimal solution of the smooth continuous function which is the imminent minimum l 0 norm by a number of iterations. At last, we verify this proposed algorithm by simulation experiments. The results denote that the proposed algorithm can achieve more excellent estimation performance than OMP (Orthogonal Matching Pursuit)algorithm under the condition of single-snapshot and can get simple process and high precision.