基于确定性压缩感知采样策略的阵列失效单元远场诊断方法

Deterministic Compressed Sensing Sampling Strategy for Diagnosis of Defective Array Elements Using Far-field Measurements

在采用压缩感知的阵列失效单元诊断方法中,结构化随机采样策略的运用对测量矩阵性能造成不利影响。针对这一问题,该文提出一种基于确定性压缩感知采样策略的阵列失效单元远场诊断方法。首先在失效单元个数满足稀疏性的前提下构造差异性阵列并将其激励作为稀疏向量,其次利用所提方法构造确定性部分傅里叶矩阵(DPFM)作为测量矩阵,最后采用l1范数最小化算法对稀疏向量进行重构,从而实现对失效单元的高概率精确诊断。理论分析和仿真实验表明,所提方法有效消除了采样位置的随机分布特性对测量矩阵性能造成的不利影响,简化了采样过程,提高了诊断成功概率。

The structured random sampling strategy adopted in array diagnosis has negative influence on the performance of measurement matrix. Therefore, a compressed sensing based deterministic sampling strategy to diagnose defective array elements using far-field measurements is investigated in this paper. In the case of the number of failed elements satisfies sparsity, the sparse vector is constructed by subtracting incentives of reference array without failures and the array under test. Deterministic Partial Fourier Matrix （DPFM） is then formulated by the proposed strategy as the measurement matrix. Finally, accurate diagnosis with high probability is achieved by 11 norm minimization. Theoretical analysis and simulation results demonstrate that the proposed method can avoid the adverse impact on the performance of measurement matrix effectively arising from the random distribution of sampling positions, simplify the sampling procedure and improve the probability of success rate of diagnosis.