浙江大学学报（理学版）2026，Vol.53Issue(4)：480-489,10.DOI:10.3785/1008-9497.25032
基于线性阵列解析式快速筛选最小冗余线性阵列(MRLA)
Rapid screening of minimum redundancy linear arrays(MRLA)based on linear array analytical formula
摘要
Abstract
Minimum redundancy linear arrays(MRLA)are widely used in wireless communication,radar,and other fields.However,obtaining data for large array sizes(with more than 28 elements and a maximum continuous baseline length exceeding 244)poses significant challenges,and there is currently a lack of clear criteria for identifying MRLA.To address this issue,this study delves into the definitions of low-redundancy linear arrays and MRLA,revealing mathematical equivalencies between the numerical values of MRLA and the scale values of perfect sparse rulers,the smallest constrained differences in element counts,and the labeling values of minimal graceful graphs.It is proven that all linear arrays have a redundancy of no less than 1.000 0,and the redundancy is strictly greater than 1.000 0 when the number of elements is 5 or more.For MRLA with a maximum continuous baseline length of L and a number of elements n,the number of elements for an MRLA of length L+1 is no more than n+1.Based on an analysis of large-scale MRLA data,the following hypothesis is proposed:linear arrays with a redundancy of no more than 1.500 0 are MRLA.To tackle the challenge of obtaining MRLA data,this study proposes an efficient screening method based on analytical formulas for linear arrays,successfully identifying four types of infinite configuration patterns for linear arrays with a redundancy of no more than 1.500 0,with a flexible upper limit for screening redundancy.This method enhances the efficiency of obtaining MRLA,laying a solid foundation for their promotion in practical applications.关键词
线性阵列/完美稀疏尺/最小冗余线性阵列/冗余度/受限差基/极小优美图Key words
linear array/perfect sparse ruler/minimum redundancy linear array(MRLA)/redundancy/constrained difference set/minimal graceful graph分类
信息技术与安全科学引用本文复制引用
唐保祥,任韩..基于线性阵列解析式快速筛选最小冗余线性阵列(MRLA)[J].浙江大学学报（理学版）,2026,53(4):480-489,10.基金项目
国家自然科学基金项目(11171114). （11171114）
