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

SHI Changan; LIU Yimin; WANG Xiqin; YU Peng

doi:10.11999/JEIT151377

Volume 38 Issue 9

Sep. 2016

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SHI Changan, LIU Yimin, WANG Xiqin, YU Peng. Optimal Allocation of Shared Aperture in Radar-communication Integrated System Based on Pareto Optimality[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2351-2357. doi: 10.11999/JEIT151377

Citation:

SHI Changan, LIU Yimin, WANG Xiqin, YU Peng. Optimal Allocation of Shared Aperture in Radar-communication Integrated System Based on Pareto Optimality[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2351-2357. doi: 10.11999/JEIT151377

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

doi: 10.11999/JEIT151377

1.
2.
(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)

Funds:

The National Natural Science Foundation of China (61571260)

Received Date: 2015-12-08
Rev Recd Date: 2016-05-13
Publish Date: 2016-09-19

Abstract

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

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References(29)

References

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.

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