阵列失效单元非凸压缩感知平面近场快速诊断方法

在阵列失效单元压缩感知近场诊断方法中,缺乏观测矩阵是否满足约束等距特性的先验信息,因此采用l1范数极小化凸优化算法将无法确保阵列失效单元的高概率精确诊断。针对该缺陷,提出了采用迭代重加权最小二乘的非凸压缩感知平面近场快速诊断方法。在失效单元个数远远小于单元总数的前提下,按照随机欠采样方式分别获取完好阵列和失效阵列的近场幅相信息,继而构造差异性阵列并利用所提的非凸优化算法对该阵列的激励进行重构,从而实现阵列失效单元的高概率精确诊断。数值仿真实验表明,所提方法不仅避免了观测矩阵约束等距特性的缺失对诊断性能造成的不利影响,而且克服了非凸范数易于陷入局部最优解这一弊端,明显缩短了诊断时间,有效提高了诊断成功概率。

Fast diagnosis approach for defective array elements using non convexcompressed sensing with planar near field measurements

The lack of apriori information on the restricted isometry property (RIP) of the observation matrix in near field scenario can not guarantee an accurate diagnosis with a high probability when using the l1 norm minimization. In order to overcome this deficiency, a fast diagnosis method with non convex compressed sensing and planar near field measurements for array diagnosis utilizing iteratively reweighted least squares algorithm is explored in this paper. Taking into account that the number of failed elements is far less than that of the total array elements, the near field data of a healthy array and a failed array are acquired by the probe using the random under sampling strategy. Then the differential array is constructed. Finally, the sparse incentive is recovered through the proposed method and the goal of array diagnosis is achieved. Numerical simulation results indicate that the proposed approach not only avoids the adverse influence on the performance of diagnosis due to the lack of RIP information, but also overcomes the problem of local minima of the non convex norm, therefore reduces the diagnosis time and improves the probability of the success rate of diagnosis effectively.