基于帕累托最优的雷达-通信共享孔径研究

石长安; 刘一民; 王希勤; 于鹏

doi:10.11999/JEIT151377

基于帕累托最优的雷达-通信共享孔径研究

doi: 10.11999/JEIT151377

2.
(清华大学电子工程系北京 100084) ②(中国洛阳电子装备试验中心洛阳 471003)

基金项目:

国家自然科学基金(61571260)

计量
- 文章访问数: 1667
- HTML全文浏览量: 135
- PDF下载量: 392
- 被引次数: 0
出版历程
- 收稿日期: 2015-12-08
- 修回日期: 2016-05-13
- 刊出日期: 2016-09-19

Optimal Allocation of Shared Aperture in Radar-communication Integrated System Based on Pareto Optimality

2.
(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)

Funds:

The National Natural Science Foundation of China (61571260)

摘要

摘要: 针对雷达-通信综合射频系统，该文提出一种基于环境信息的共享孔径动态分配方法。首先基于帕累托最优理论将共享孔径分配建模为一个多目标优化问题，并建立了雷达阵列方向图的峰值旁瓣电平和多输入多输出(MIMO)通信系统的信道容量两个优化目标函数。然后提出一种基于整数编码的改进粒子群算法，通过迭代求解以帕累托前沿的形式给出一组最优解，供决策者根据任务需求从中选出一个最满意的解。最后，仿真结果验证了该方法的有效性。
- 共享孔径 /
- 综合射频系统 /
- 多目标优化 /
- 粒子群算法 /
- 帕累托最优
Abstract: In this work, considering a radar-communication integrated radio frequency system, a dynamic allocation method of shared aperture using relevant environmental information is proposed. Firstly, the shared aperture allocation task is formulated as a Multi-Objective Optimization (MOO) problem based on Pareto optimality, which uses the peak side-lobe level of radar array pattern and the channel capacity of Multiple Input Multiple Output (MIMO) communication system as its objective function. Then, an improved particle swarm optimization algorithm based on integer encoding is proposed to solve the MOO problem. The iterative algorithm can find out a set of optimal solutions in the form of Pareto front, one of which can be chosen by decision makers as the most satisfactory solution according to mission requirements. Finally, the simulation results verify the effectiveness of the proposed method.
- Shared aperture /
- Integrated radio frequency system /
- Multi-objective optimization /
- Particle swarm optimization /
- Pareto optimality

HTML全文

参考文献(29)

TAVIK G C, HILTERBRICK C L, EVINS J B, et al. The advanced multifunction RF concept[J]. IEEE Transactions on Microwave Theory and Techniques, 2005, 53(3): 1009-1020. doi: 10.1109/TMTT.2005.843485.

张明友. 雷达-电子战-通信一体化概论[M]. 北京: 国防工业出版社, 2010: 1-15.

ZHANG Mingyou. The Conspectus of Integrated Radar- Electronic Warfare-Communication[M]. Beijing: National Defense Industry Press, 2010: 1-15.

吴远斌. 多功能射频综合一体化技术的研究[J]. 现代雷达, 2013, 35(8): 70-74.

WU Yuanbin. Research on technology of multifunction radio frequency integration[J]. Modern Radar, 2013, 35(8): 70-74.

QUAN Siji, QIAN Weiping, GUO Junhai, et al. Radar- communication integration: An overview[C]. 2014 IEEE 7th International Conference on Advanced Infocomm Technology (ICAIT), Fuzhou, China, 2014: 98-103. doi: 10.1109/ ICAIT.2014.7019537.

胡元奎, 靳学明, 范忠亮. 多功能综合射频系统技术研究[J]. 雷达科学与技术, 2015, 13(3): 233-239. doi: 10.3969/j.issn. 1672-2337.2015.03.003.

HU Yuankui, JIN Xueming, and FAN Zhongliang. Research on multi-function integrated RF system technology[J]. Radar Science and Technology, 2015, 13(3): 233-239. doi: 10.3969/ j.issn.1672-2337.2015.03.003.

KHODIER M M and CHRISTODOULOU C G. Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization[J]. IEEE Transactions on Antennas and Propagation, 2005, 53(8): 2674-2679. doi: 10.1109/TAP.2005.851762.

HA B V, ZICH R E, MUSSETTA M, et al. Thinned array optimization by means of M-cGA[C]. 2014 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Tennessee, USA, 2014: 1956-1957. doi: 10.1109/APS.2014.6905305.

WANG Xiangrong, ABOUTANIOS E, and AMIN M G. Thinned array beampattern synthesis by iterative soft- thresholding-based optimization algorithms[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(12): 6102-6113. doi: 10.1109/TAP.2014.2364048.

严韬, 陈建文, 鲍拯. 基于改进遗传算法的天波超视距雷达二维阵列稀疏优化设计[J]. 电子与信息学报, 2014, 36(12): 3014-3020. doi: 10.3724/SP.J.1146.2013.02011.

YAN Tao, CHEN Jianwen, and BAO Zheng. Optimization design of sparse 2-D arrays for over-the-horizon radar (OTHR) based on improved genetic algorithm[J]. Journal of Electronics Information Technology, 2014, 36(12): 3014-3020. doi: 10.3724/SP.J.1146.2013.02011.

FOSCHINI G J and GANS M J. On limits of wireless communications in a fading environment when using multiple antennas[J]. Wireless Personal Communications, 1998, 6(3): 311-335. doi: 10.1109/TVT.2014.2363170.

TELATAR E. Capacity of multi-antenna gaussian channels [J]. European Transactions on Telecommunications, 1999, 10(6): 585-595. doi: 10.1002/ett.4460100604.

POURAHMADI V, KOHANDANI F, and MOBASHER A. On the accuracy of channel modeling based on the Kronecker product[C]. 2010 IEEE 72nd Vehicular Technology Conference Fall (VTC 2010-Fall), Ottawa, Canada, 2010: 1-5. doi: 10.1109/VETECF.2010.5594341.

LOYKA S L. Channel capacity of MIMO architecture using the exponential correlation matrix[J]. IEEE Communications Letters, 2001, 5(9): 369-371. doi: 10.1109/4234.951380.

GOROKHOV A. Antenna selection algorithms for MEA transmission systems[C]. 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Orlando, FL, USA, 2002, III: 2857-2860. doi: 10.1109/ ICASSP.2002.5745244.

SANAYEI S and NOSRATINIA A. Capacity of mimo channels with antenna selection[J]. IEEE Transactions on Information Theory, 2007, 53(11): 4356-4362. doi: 10.1109/ TIT.2007.907476.

REYES-SIERRA M and COELLO C C. Multi-objective particle swarm optimizers: a survey of the state-of-the-art[J]. International Journal of Computational Intelligence Research, 2006, 2(3): 287-308.

RAQUEL C R and NAVAL Jr P C. An effective use of crowding distance in multiobjective particle swarm optimization[C]. The 7th Annual conference on Genetic and Evolutionary Computation, Washington, DC, USA, 2005: 257-264. doi: 10.1145/1068009.1068047.

KENNEDY J and EBERHART R. Particle swarm optimization[C]. IEEE International Conference on Neural Networks, 1995, 4: 1942-1948. doi: 10.1109/ICNN.1995. 488968.

NANBO J and RAHMAT-SAMII Y. Advances in particle swarm optimization for antenna designs: real-number, binary, single-objective and multiobjective implementations[J]. IEEE Transactions on Antennas and Propagation, 2007, 55(3): 556-567. doi: 10.1109/TAP.2007.891552.

YUAN Quan and YIN G. Analyzing convergence and rates of convergence of particle swarm optimization algorithms using stochastic approximation methods[J]. IEEE Transactions on Automatic Control, 2015, 60(7): 1760-1773. doi: 10.1109/ TAC.2014.2381454.

KNOWLES J and CORNE D. Approximating the nondominated front using the pareto archived evolution strategy[J]. Evolutionary Computation, 2000, 8(2): 149-172. doi: 10.1162/106365600568167.

MAHETA H H and DABHI V K. An improved SPEA2 multi objective algorithm with non dominated elitism and generational crossover[C]. 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, 2014: 75-82. doi: 10.1109/ ICICICT.2014.6781256.

KARIMI F and LOTFI S. Solving multi-objective problems using SPEA2 and Tabu search[C]. 2014 Iranian Conference on Intelligent Systems (ICIS), Bam, Iran, 2014: 1-6. doi: 10.1109/IranianCIS.2014.6802566.

DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. doi: 10.1109/4235.996017.

KONAK A, COIT D W, and SMITH A E. Multi-objective optimization using genetic algorithms: A tutorial[J]. Reliability Engineering System Safety, 2006, 91(9): 992-1007. doi: 10.1016/j.ress.2005.11.018.

施引文献

资源附件(0)

访问统计