基于函数−特征子空间的水下高分辨方位估计算法

The high-resolution underwater azimuth estimation algorithm for function-feature subspaces

针对有限阵列孔径下传统方位估计方法对低频目标方位分辨能力弱的问题,提出一种基于函数−特征子空间理论的高分辨方位估计算法。该方法在特征子空间算法的基础上,改进其投影扫描向量的子空间分解形式,并在计算流程中引入函数波束形成算法,获得基于指数控制的函数−特征子空间方位估计算法。理论分析与仿真结果表明,该算法涵盖了常规的特征子空间算法,且当指数绝对值小于1时,该算法在保证抗噪性能的前提下,空间方位谱具有更低的旁瓣和更窄的主瓣。试验数据结果表明,该算法在分辨能力方面优于特征子空间等波束形成方法,具有较好的实际应用前景。

In response to the problem of weak azimuth resolution of traditional azimuth estimation methods under limited array apertures,a high-resolution azimuth estimation algorithm based on the theory of function-feature subspace is proposed.The proposed method enhances the subspace decomposition form of the eigenspace algorithm by improving the projection scanning vectors in the eigenspace algorithm and incorporates the functional beamforming algorithm during the calculation process to achieve azimuth estimation in the function-feature subspace based on exponential control.Theoretical analysis and simulation results demonstrate that the algorithm encompasses the conventional eigenspace algorithm and provides improved sidelobe suppression and narrower main lobe width,while maintaining noise resistance performance.Experimental data results indicate that this algorithm surpasses eigenspace and other beamforming methods in terms of resolution capability,making it highly promising for practical applications.