Qiping Zhang , Xin Tai , Yongfeng Zuo , Hua Wang , Jinfeng Hu
{"title":"Codesign of constant modulus waveform and receive filters for FDA-MIMO radar","authors":"Qiping Zhang , Xin Tai , Yongfeng Zuo , Hua Wang , Jinfeng Hu","doi":"10.1016/j.dsp.2025.105542","DOIUrl":null,"url":null,"abstract":"<div><div>The joint design of waveform and receive filter is one of the important technologies currently being studied in Frequency diverse array multi-input multi-output (FDA-MIMO) radar. The problem model studied in this paper is to maximize the signal-to-interference-noise ratio (SINR) of the system under the constant modulus constraint of the waveform and the norm constraint of the filter. The problem is non-convex, which brings challenges to its solution. Existing methods use relaxation-based methods to solve this problem, but this will inevitably introduce relaxation errors. To solve the above problems, we notice that the complex circle-sphere manifold space (CCSMS) can naturally satisfy the constant modulus constraint and norm constraint. Based on this feature, the problem becomes an unconstrained optimization problem on the CCSMS manifold, eliminating the need for relaxation. The Riemannian conjugate gradient algorithm can then be directly applied to solve the waveform and receive filter in parallel. We compared it with the existing methods through simulation: 1) SINR was improved by <span><math><mn>4</mn><mspace></mspace><mrow><mi>dB</mi></mrow></math></span>; 2) the computational complexity was reduced compared with existing methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105542"},"PeriodicalIF":3.0000,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425005640","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The joint design of waveform and receive filter is one of the important technologies currently being studied in Frequency diverse array multi-input multi-output (FDA-MIMO) radar. The problem model studied in this paper is to maximize the signal-to-interference-noise ratio (SINR) of the system under the constant modulus constraint of the waveform and the norm constraint of the filter. The problem is non-convex, which brings challenges to its solution. Existing methods use relaxation-based methods to solve this problem, but this will inevitably introduce relaxation errors. To solve the above problems, we notice that the complex circle-sphere manifold space (CCSMS) can naturally satisfy the constant modulus constraint and norm constraint. Based on this feature, the problem becomes an unconstrained optimization problem on the CCSMS manifold, eliminating the need for relaxation. The Riemannian conjugate gradient algorithm can then be directly applied to solve the waveform and receive filter in parallel. We compared it with the existing methods through simulation: 1) SINR was improved by ; 2) the computational complexity was reduced compared with existing methods.
期刊介绍:
Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.
The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:
• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,