针对传统均匀线阵中四阶累积量计算复杂度大、对快拍数敏感的问题,提出了一种快速去冗余的高分辨波达方向估计新方法。该方法首先通过构造选择矩阵对四阶累积量矩阵进行第1次降维处理,摒弃传统四阶累积量中大量冗余数据,然后对无冗余累积量矩阵进行矢量化并通过二次降维得到统计性能更优的向量观测模型,最后在相应的过完备基下建立观测模型的稀疏表示进行波达方向(Direction of Arrival,DOA)估计。同时将方法推广到L型阵列2维DOA估计,扩展了其应用范围。与传统的四阶累积量方法相比,该方法大大地减小了计算量,对快拍数要求不高,并且能够有效地抑制相关色噪声。理论分析和仿真实验验证了该方法对1维和2维DOA估计都具有较高的估计精度和分辨率。 Aiming at the problem that the fourth-order cumulants in conventional uniform linear arrays are computationally complex and sensitive to the number of snapshots, a fast and de-redundant high-resolution direction-of-arrival estimation method is proposed. In this method, the first dimensionality reduction of the fourth-order cumulant matrix is ​​constructed by constructing a selection matrix, and a large amount of redundant data in the traditional fourth-order cumulants are discarded. Then the non-redundant cumulant matrix is ​​vectorized and reduced by the second dimensionality reduction Get the vector observation model with better statistical performance, and finally establish the Direction of Arrival (DOA) estimation of the sparse representation of the observation model under the corresponding overcomplete basis. At the same time, the method is extended to the L-shaped array two-dimensional DOA estimation, which expands the scope of its application. Compared with the traditional fourth-order cumulant method, this method greatly reduces the amount of computation, lowers the number of snapshots and can effectively suppress the correlated color noises. Theoretical analysis and simulation results show that this method has higher estimation accuracy and resolution for 1-D and 2-D DOA estimation.