| 121 | 0 | 1185 |
| 下载次数 | 被引频次 | 阅读次数 |
针对单载波雷达通信一体化系统射频资源分配灵活性不足的问题，提出面向多目标探测与多用户通信的多载波雷达通信一体化射频资源优化算法。首先，基于多载波雷达通信一体化信号模型，分别推导表征多目标探测性能的雷达条件互信息解析表达式和表征多用户通信性能的最高可达速率解析表达式。其次，以最小化多载波雷达通信一体化系统的总发射功率为优化目标，以满足预先设定的雷达条件互信息阈值和通信最高可达速率阈值为约束条件，构建多载波雷达通信一体化射频资源优化模型。针对所构建的非凸优化模型，提出基于凸松弛的交替优化算法，通过对约束条件中多个非线性不等式约束进行凸松弛，采用循环最小法和Karush-Kuhn-Tucker(KKT)条件实现迭代求解。仿真结果表明，与现有算法相比，所提算法能在满足多目标探测与多用户通信性能的条件下，能有效降低雷达通信一体化系统的总发射功率，提升其射频隐身性能。
Abstract：To address the limitation of insufficient flexibility of radio frequency resource allocation in single-carrier Dual-Function Radar-Communication(DFRC) system, a radio frequency resource optimization algorithm for multi-target detection and multi-user communication of multi-carrier DFRC system is proposed. Firstly, based on the signal model of a multi-carrier DFRC system, the analytical expression of radar conditional mutual information characterizing the multi-target detection performance and the analytical expression of data transmission rate characterizing the multi-user communication performance were derived, respectively. Secondly, with minimizing the total transmission power of the multi-carrier DFRC system as the optimization objective, and radar conditional mutual information and communication data transmission rate as constraints, a multi-carrier radio frequency resource optimization model was constructed. For the constructed non-convex optimization model, an alternating optimization algorithm based on convex relaxation is proposed. This approach involves convex relaxation of multiple nonlinear inequality constraints, and employs the cyclic minimization method combined with Karush-Kuhn-Tucker(KKT) condition for iterative problem-solving. The simulation results show that, compared with the existing algorithms, the proposed algorithm can achieve lower transmission power under the condition of multi-target detection and multi-user communication, and improve the radio frequency stealth performance of the DFRC system.
[1]孙备，王迎龙，卜德森，等.低慢小无人机目标探测关键技术与发展趋势[J].无人系统技术，2025, 8(1):20-30.SUN B, WANG Y L, BU D S, et al. Technologies and developments of low slow small UAV target detection[J]. Unmanned Systems Technology, 2025, 8(1):20-30(in Chinese).
[2]姜剑雄，桂阳，李琴，等.反无人机集群态势感知应对策略研究[J].无人系统技术，2024, 7(4):47-54.JIANG J X, GUI Y, LI Q, et al. Research on situation awareness response strategy for anti UAV swarm[J].Unmanned Systems Technology, 2024, 7(4):47-54(in Chinese).
[3]李骞，唐堂，王叶群，等.可见光辅助无人机通信网络容量优化方法[J].无人系统技术，2024, 7(3):28-39.LI Q, TANG T, WANG Y Q, et al. Research on capacity optimization for visible light-assisted UAV communication networks[J]. Unmanned Systems Technology,2024, 7(3):28-39(in Chinese).
[4]梁兴东，李强，王杰，等.雷达通信一体化技术研究综述[J].信号处理，2020, 36(10):1615-1627.LIANG X D, LI Q, WANG J, et al. Joint wireless communication and radar sensing:Review and future prospects[J]. Journal of Signal Processing, 2020, 36(10):1615-1627(in Chinese).
[5]李轩，周逸潇，赵尚弘，等.雷达通信一体化资源复用、波形共享和微波光子研究进展[J].信号处理，2023, 39(12):2115-2130.LI X, ZHOU Y X, ZHAO S H, et al. Development of resource multiplexing, waveform sharing and microwave photonics for joint radar and communication[J]. Journal of Signal Processing, 2023, 39(12):2115-2130(in Chinese).
[6] LI J P, SHAO X D, CHEN F, et al. Networked integrated sensing and communications for 6G wireless systems[J]. IEEE Internet of Things Journal, 2024, 11(17):29062-29075.
[7]王先梅，刘向博，任语铮，等.人工智能驱动的雷达通信一体化研究综述[J].雷达学报，2015, 14(4):1071-1091.WANG X M, LIU X B, REN Y Z, et al. Review of research on artificial intelligence-driven joint radar communication[J]. Journal of Radars, 2015, 14(4):1071-1091(in Chinese).
[8] YANG L, WEI Y F, FENG Z Y, et al. Deep reinforcement learning-based resource allocation for integrated sensing, communication, and computation in vehicular network[J]. IEEE Transactions on Wireless Communications, 2024, 23(12):18608-18622.
[9] MOHAMMADKARIMI M, RAJAN R T. Cooperative sense and avoid for UAVs using secondary radar[J].IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(5):7041-7055.
[10] CHU N H, HOANG D T, NGUYEN D N, et al. Joint speed control and energy replenishment optimization for UAV-assisted IoT data collection with deep reinforcement transfer learning[J]. IEEE Internet of Things Journal, 2023, 10(7):5778-5793.
[11] JOHANNES W, STANKO S, KALLFASS I. Efficient joint broadband radar and single carrier communication system in frequency division multiplexing for high-range applications[C]//2023 24th International Radar Symposium(IRS), Berlin, Germany, May 24-26, 2023.
[12] ZENG Y H, MA Y G, SUN S M. Joint radar-communication with cyclic prefixed single carrier waveforms[J].IEEE Transactions on Vehicular Technology, 2020, 69(4):4069-4079.
[13]李梦姣.雷达通信一体化波形设计研究[D].成都：电子科技大学，2018.LI M J. Research on waveform design of integrated radar and communication[D]. Chengdu:University of Electronic Science and Technology of China, 2018(in Chinese).
[14]黄驿轩.通信雷达一体化波形设计及信号处理[D].成都：电子科技大学，2022.HUANG Y X. Waveform design and signal processing for fusion of communications and radar[D]. Chengdu:University of Electronic Science and Technology of China, 2022(in Chinese).
[15]李晓柏，杨瑞娟，程伟，等.新的互补序列在雷达通信一体化中的应用[J].系统工程与电子技术，2021, 43(3):693-699.LI X B, YANG R J, CHENG W, et al. Application of a novel complementary signal to integrated radar and communication[J]. Systems Engineering and Electronics,2021, 43(3):693-699(in Chinese).
[16]肖博. OFDM雷达通信一体化波形设计与处理技术研究[D].长沙：国防科学技术大学，2022.XIAO B. Research on waveform design and processing technique of OFDM radar communication integration[D]. Changsha:National University of Defense Technology, 2022(in Chinese).
[17]马丁友，刘祥，黄天耀，等.雷达通信一体化：共用波形设计和性能边界[J].雷达学报，2022, 11(2):198-212.MA D Y, LIU X, HUANG T Y, et al. Joint radar and communications:Shared waveform designs and performance bounds[J]. Journal of Radars, 2022, 11(2):198-212(in Chinese).
[18] BIC?M, KOIVUNEN V. Radar waveform optimization for target parameter estimation in cooperative radar-communications systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(5):2314-2326.
[19] LIU F, LIU Y F, LI A, et al. Cramér-Rao bound optimization for joint radar-communication beamforming[J]. IEEE Transactions on Signal Processing, 2022,70:240-253.
[20] ZHANG H W, LIU W J, ZHANG Q, et al. A robust joint frequency spectrum and power allocation strategy in a coexisting radar and communication system[J].Chinese Journal of Aeronautics, 2024, 37(10):393-409.
[21] LUONG N C, LU X, HOANG D T, et al. Radio resource management in joint radar and communication:A comprehensive survey[J]. IEEE Communications Surveys&Tutorials, 2021, 23(2):780-814.
[22] ZHOU Y F, ZHOU H L, ZHOU F H, et al. Resource allocation for a wireless powered integrated radar and communication system[J]. IEEE Wireless Communications Letters, 2019, 8(1):253-256.
[23] SHI C G, WANG F, SALOUS S, et al. Joint subcarrier assignment and power allocation strategy for integrated radar and communications system based on power minimization[J]. IEEE Sensors Journal, 2019, 19(23):11167-11179.
[24]李琬璐，相征，任鹏.基于FBMC信号的低截获雷达通信一体化波形设计[J].雷达学报，2023, 12(2):287-296.LI W L, XIANG Z, REN P. Filter bank multi-carrier waveform design for low probability of intercepting joint radar and communication system[J]. Journal of Radars, 2023, 12(2):287-296(in Chinese).
[25] GONG P C, XU K Y, WU Y T, et al. Optimization of LPI-FDA-MIMO radar and MIMO communication for spectrum coexistence[J]. IEEE Wireless Communications Letters, 2023, 12(6):1076-1080.
[26]胡邓雨，谢锐，罗锴，等. OFDM雷达通信一体化的同频干扰抑制技术[J].信号处理，2022, 38(11):2320-2331.HU D Y, XIE R, LUO K, et al. Co-channel interference suppression for OFDM integrated radar and communication[J]. Journal of Signal Processing, 2022, 38(11):2320-2331(in Chinese).
[27]刘凡，袁伟杰，原进宏，等.雷达通信频谱共享及一体化：综述与展望[J].雷达学报，2021, 10(3):467-484.LIU F, YUAN W J, YUAN J H, et al. Radar-communication spectrum sharing and integration:Overview and prospect[J]. Journal of Radars, 2021, 10(3):467-484(in Chinese).
[28]袁野，杨剑，刘辛雨，等.基于任务效用最大化的多雷达协同任务规划算法[J].雷达学报，2023, 12(3):550-562.YUAN Y, YANG J, LIU X Y, et al. Multiradar collaborative task planning based on task utility maximization[J]. Journal of Radars, 2023, 12(3):550-562(in Chinese).
基本信息:
DOI：10.19942/j.issn.2096-5915.2025.05.48
中图分类号:TN95
引用信息:
[1]陈滢,武浩正,时晨光,等.多载波雷达通信一体化射频资源优化算法[J].无人系统技术,2025,8(05):95-107.DOI:10.19942/j.issn.2096-5915.2025.05.48.
基金信息:
国家自然科学基金(62271247); 江苏省自然科学基金优秀青年基金项目(BK20240181)
2025-06-15
2025
2025-07-17
2025
1
2025-10-15
2025-10-15