Adaptive p‐norm weighted cyclic algorithm‐new algorithm for designing unimodular multiple input multiple output radar waveforms with good correlations

• Published in: Electronics letters 2023-04, Vol.59 (8), p.n/a
• Main Authors: Liu, Tianqu, Sun, Jinping, Wang, Guohua, Du, Xiaoyong, Hu, Weidong
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Algorithms; Correlation; Iterative methods; Lower bounds; MIMO radar; Optimization; Optimization models; Set design; signal processing; Waveforms
• ISSN: 0013-5194; 1350-911X
• DOI: 10.1049/ell2.12799

This letter formulates a high‐dimensional multi‐objective optimization model to minimize all correlation function values for multiple input multiple output (MIMO) radar waveforms. Then, to solve this problem, this letter proposes an adaptive weighted cyclic algorithm‐new (CAN), named p‐norm weighted cyclic algorithm‐new (p‐WeCAN). Specifically, the adaptive weight updating function is constructed using p‐norm to minimize peak side‐lobe level (PSL) and integrated side‐lobe level (ISL) simultaneously. Numerical results show that the p‐norm weighted cyclic algorithm‐new obtains similar PSL values as the best PSL minimization algorithm and low integrated side‐lobe level values close to its lower bound at the same time. To minimize peak side‐lobe level (PSL) and integrated side‐lobe level (ISL) metrics for unimodular multiple input multiple output (MIMO) radar waveforms simultaneously, this letter formulates a multi‐objective optimization model and develops an adaptive weighted cyclic algorithm‐new (CAN), named p‐norm weighted cyclic algorithm‐new (p‐WeCAN) to solve this problem. The authors proposed the p‐norm adaptive weights updating function is able to minimize the PSL and ISL metrics at the same time.