In numerous measurement scenarios within the aerospace domain,signal phase informa-tion often faces susceptibility to degradation.This article endeavors to address the challenge of phase retrieval amidst the presence of outliers.Leveraging the robustness of the median against outliers,it serves as a standard regularization technique to intercept phaseless measurements.However,this interception can potentially reduce available measurements and escalate measurement complexity.To surmount this obstacle,a novel weighted median truncation algo-rithm is proposed.This algorithm integrates weights that aptly capture the likelihood of referring to the original signal,thereby delving deeper into the available information of unmeasured values.Comparative analysis reveals that this approach offers superior measurement complexity and resili-ence against outlier interference when juxtaposed with similar algorithms.
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