{"title":"JSMWO: Joint Radar Spectrum Management and Waveform Optimization Strategy for Maneuvering Target Tracking in Congested Spectrum Environment","authors":"Yibo Zhang;Yang Li;Chunmao Ye;Yunrong Zhu;Wenbo Ding;Xiao Xu","doi":"10.1109/TAES.2024.3486259","DOIUrl":null,"url":null,"abstract":"Waveform-agile tracking performance of cognitive tracking radar is significantly affected by the target maneuverability and increasing radio frequency interference (RFI) of other in-band systems. Therefore, a joint spectrum management and waveform optimization (JSMWO) policy is proposed to improve the maneuvering target tracking performance while mitigating RFI. First, the JSMWO policy is mathematically formulated to simultaneously optimize the spectrum occupancy and waveform parameters by minimizing a JSMWO-oriented three-term objective function. Specifically, the first two terms consider the tradeoff between interference avoidance and bandwidth utilization, and the impact of current spectrum occupancy action on future multistep rewards. The last subreward term incorporates the criterion-specific conditional predictive Bayesian risk, waveform library limitation, and real-time spectrum constraint generated by spectrum occupancy action. Second, to resolve the underlying mixed-integer, nonlinear, nonconvex multivariable optimization problem, by proper coupling analysis, we develop a four-stage iterative method to decompose the original JSMWO policy into two subpolicies and tackle them sequentially, which encompasses the deep Q-learning (DQL) based spectrum occupancy optimization, particle swarm optimization (PSO) based waveform optimization, and feedback information-based online learning. Specifically, the dual-network architecture and experience replay mechanism equipped in DQL can effectively address the delay reward and future multistep effects during the spectrum occupancy subpolicy optimization. Waveform optimization subpolicy is subsequently reformulated as an equivalent constrained optimization problem, wherein the PSO can effectively optimize the waveform parameters through interparticle interactions and iterations while satisfying the spectrum constraint and waveform limitation. Simulation results demonstrate the effectiveness and superiority of the above-mentioned JSMWO policy.","PeriodicalId":13157,"journal":{"name":"IEEE Transactions on Aerospace and Electronic Systems","volume":"61 2","pages":"3425-3439"},"PeriodicalIF":5.7000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Aerospace and Electronic Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10740029/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0
Abstract
Waveform-agile tracking performance of cognitive tracking radar is significantly affected by the target maneuverability and increasing radio frequency interference (RFI) of other in-band systems. Therefore, a joint spectrum management and waveform optimization (JSMWO) policy is proposed to improve the maneuvering target tracking performance while mitigating RFI. First, the JSMWO policy is mathematically formulated to simultaneously optimize the spectrum occupancy and waveform parameters by minimizing a JSMWO-oriented three-term objective function. Specifically, the first two terms consider the tradeoff between interference avoidance and bandwidth utilization, and the impact of current spectrum occupancy action on future multistep rewards. The last subreward term incorporates the criterion-specific conditional predictive Bayesian risk, waveform library limitation, and real-time spectrum constraint generated by spectrum occupancy action. Second, to resolve the underlying mixed-integer, nonlinear, nonconvex multivariable optimization problem, by proper coupling analysis, we develop a four-stage iterative method to decompose the original JSMWO policy into two subpolicies and tackle them sequentially, which encompasses the deep Q-learning (DQL) based spectrum occupancy optimization, particle swarm optimization (PSO) based waveform optimization, and feedback information-based online learning. Specifically, the dual-network architecture and experience replay mechanism equipped in DQL can effectively address the delay reward and future multistep effects during the spectrum occupancy subpolicy optimization. Waveform optimization subpolicy is subsequently reformulated as an equivalent constrained optimization problem, wherein the PSO can effectively optimize the waveform parameters through interparticle interactions and iterations while satisfying the spectrum constraint and waveform limitation. Simulation results demonstrate the effectiveness and superiority of the above-mentioned JSMWO policy.
期刊介绍:
IEEE Transactions on Aerospace and Electronic Systems focuses on the organization, design, development, integration, and operation of complex systems for space, air, ocean, or ground environment. These systems include, but are not limited to, navigation, avionics, spacecraft, aerospace power, radar, sonar, telemetry, defense, transportation, automated testing, and command and control.