A Learning-Inspired Strategy to Design Binary Sequences With Good Correlation Properties: SISO and MIMO Radar Systems

• Published in: IEEE transactions on aerospace and electronic systems Vol. 59; no. 5; pp. 1 - 19
• Main Authors: Rezaei, Omid, Ahmadi, Mahdi, Naghsh, Mohammad Mahdi, Aubry, Augusto, Nayebi, Mohammad Mahdi, De Maio, Antonio
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-10-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Binary sequence design; Codes; complementary sets of sequence (CSS); Correlation; integrated sidelobe level (ISL); Learning; machine learning; MIMO communication; MIMO radar; multiple - input multiple - output (MIMO); Optimization; peak sidelobe level (PSL); Radar; Radar equipment; Sidelobes; single - input single - output (SISO); SISO (control systems); SISO communication
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2023.3280468

In this paper, the design of binary sequences exhibiting low values of aperiodic/periodic correlation functions, in terms of Integrated Sidelobe Level (ISL), is pursued via a learning-inspired method. Specifically, the synthesis of either a single or a burst of codes is addressed, with reference to both Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) radar systems. Two optimization machines, referred to as two-layer and single-layer Binary Sequence Correlation Network (BiSCorN), able to learn actions to design binary sequences with small ISL/Complementary ISL (CISL) for SISO and MIMO systems are proposed. These two networks differ in terms of the capability to synthesize Low-Correlation-Zone (LCZ) sequences and computational cost. Numerical experiments show that proposed techniques can outperform state-of-the-art algorithms for the design of binary sequences and Complementary Sets of Sequences (CSS) in terms of ISL and, interestingly, of Peak Sidelobe Level (PSL).