JSMWO: Joint Radar Spectrum Management and Waveform Optimization Strategy for Maneuvering Target Tracking in Congested Spectrum Environment

IF 5.7 2区 计算机科学 Q1 ENGINEERING, AEROSPACE IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2024-11-01 DOI:10.1109/TAES.2024.3486259
Yibo Zhang;Yang Li;Chunmao Ye;Yunrong Zhu;Wenbo Ding;Xiao Xu
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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.
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JSMWO:联合雷达频谱管理和波形优化策略,用于在拥塞频谱环境中进行机动目标跟踪
认知跟踪雷达的波形敏捷跟踪性能受到目标机动性和其他带内系统不断增加的射频干扰的显著影响。为此,提出了一种联合频谱管理和波形优化(JSMWO)策略,以提高机动目标跟踪性能,同时减轻RFI。首先,在数学上制定了JSMWO策略,通过最小化面向JSMWO的三项目标函数来同时优化频谱占用和波形参数。具体来说,前两个术语考虑了避免干扰和带宽利用之间的权衡,以及当前频谱占用行为对未来多步骤奖励的影响。最后一个子奖励项包含特定于标准的条件预测贝叶斯风险、波形库限制和频谱占用行为产生的实时频谱约束。其次,为了解决潜在的混合整数、非线性、非凸多变量优化问题,通过适当的耦合分析,我们开发了一种四阶段迭代方法,将原始JSMWO策略分解为两个子策略并依次处理,其中包括基于深度q -学习(DQL)的频谱占用优化、基于粒子群优化(PSO)的波形优化和基于反馈信息的在线学习。具体而言,DQL中配备的双网络架构和经验重播机制可以有效地解决频谱占用子策略优化过程中的延迟奖励和未来多步效应。将波形优化子策略重新表述为等效约束优化问题,粒子群算法在满足频谱约束和波形限制的前提下,通过粒子间相互作用和迭代有效地优化波形参数。仿真结果验证了上述JSMWO策略的有效性和优越性。
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来源期刊
CiteScore
7.80
自引率
13.60%
发文量
433
审稿时长
8.7 months
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.
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