A novel probabilistic sparsity-promoting method for robust near-field (NF) antenna characterization is proposed. It leverages on the measurements-by-design (MebD) paradigm and it exploits some a-priori information on the antenna under test (AUT) to generate an over-complete representation basis. Accordingly, the problem at hand is reformulated in a compressive sensing (CS) framework as the retrieval of a maximally-sparse distribution (with respect to the overcomplete basis) from a reduced set of measured data and then it is solved by means of a Bayesian strategy. Representative numerical results are presented to, also comparatively, assess the effectiveness of the proposed approach in reducing the "burden/cost" of the acquisition process as well as to mitigate (possible) truncation errors when dealing with space-constrained probing systems.
翻译:因此,在压缩式遥感(CS)框架内重拟了问题,以检索一组数量较少的计量数据中最粗糙的分布(在过于完整的基础上),然后通过巴伊西亚战略加以解决。 代表数字结果还相对地用于评估拟议方法在减少获取过程的“负担/成本”以及减少(可能)处理空间受限制的探测系统时的脱轨错误方面的有效性。