Microwave Staring Correlated Imaging (MSCI) is a high-resolution radar imaging modality, whose resolution is mainly determined by the randomness of radiation source. To optimize the design of random radiation source, a novel concept of temporal-spatial relative distribution entropy (TSRDE) is proposed to describe the temporal-spatial stochastic characteristics of radiation source. The TSRDE can be utilized as the optimization criterion to design the array conguration and signal parameters by means of optimization algorithms. In this paper the genetic algorithm is applied to search for the best design. Numerical simulations are performed and the results show that the TSRDE is an effective method to characterize the randomness of radiation source, and the source parameters optimized by this method can dramatically improve the imaging resolution.
1. Ausherman, D. A., A. Kozma, J. L. Walker, H. M. Jones, and E. C. Poggio, "Development in radar imaging," IEEE Trans. Aerospace and Electronic Systems, Vol. 20, 363-397, 1984. doi:10.1109/TAES.1984.4502060
3. Caorsi, S., M. Donelli, A. Lommi, and A. Massa, "Location and imaging of two-dimensional scatterers by using a particle swarm algorithm," Journal of Electromagnetic Waves and Applications, Vol. 18, 481-494, 2004. doi:10.1163/156939304774113089
4. Donelli, M., I. Craddock, D. Gibbins, and M. Sarafianou, "A three-dimensional time domain microwave imaging method for breast cancer detection based on an evolutionary algorithm," Progress In Electromagnetics Research M, Vol. 18, 179-195, 2012. doi:10.2528/PIERM11040903
5. Rocca, P., M. Donelli, G. L. Gragnani, and A. Massa, "Iterative multi-resolution retrieval of non-measurable equivalent currents for the imaging of dielectric objects," Inverse Problems, Vol. 25, 1-15, 2009.
6. Franceschini, G., M. Donelli, R. Azaro, and A. Massa, "Inversion of phaseless total field data using a two-step strategy based on the iterative multiscaling approach," IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, 3527-3539, 2006. doi:10.1109/TGRS.2006.881753
7. Guo, Y., X. He, and D. Wang, "A novel super-resolution imaging method based on stochastic radiation radar array," Measurement Science and Technology, Vol. 24, No. 7, 31-36, 2013. doi:10.1088/0957-0233/24/7/074013
8. He, X., B. Liu, and D. Wang, "A novel approach of high spatial-resolution microwave staring correlated imaging," Proceedings of 2013 Asia-Pacific Conference on Synthetic Aperture Radar, 75-78, Tsukuba, Japan, September 2013.
9. Ma, Y., X. He, and D. Wang, "Microwave staring correlated imaging and resolution analysis," Proceedings of 2013 Geo-Informatics in Resource Management and Sustainable Ecosystem International Symposium, 75-78, Wuhan, China, November 2013.
10. Li, D., X. Li, Y. Cheng, Y. Qin, and H. Wang, "Radar coincidence imaging: An instantaneous imaging technique with stochastic signals," IEEE Transactions on Geoscience Remote Sensing, Vol. 52, No. 4, 2261-2271, 2014. doi:10.1109/TGRS.2013.2258929
11. Zhu, S., A. Zhang, Z. Xu, and X. Dong, "Radar coincidence imaging with random microwave source," IEEE Antennas and Wireless Propagation Letters, Vol. 14, 1239-1242, 2015. doi:10.1109/LAWP.2015.2399977
12. Li, D., X. Li, and Y. Cheng, "Three dimensional radar coincidence imaging," Progress In Electromagnetics Research M, Vol. 33, 223-238, 2013.
13. Zhou, X., H. Wang, Y. Cheng, Y. Qin, and H. Chen, "Radar coincidence imaging for off-grid target using frequency hopping waveforms," International Journal of Antennas and Propagation, Vol. 2016, 1-16, 2016.
14. Zha, G., H. Wang, and Z. Yang, "Effect analysis and design on array geometry for coincidence imaging radar based on effective rank theory," Proceedings of 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 1-8, Xiamen, China, April 2015.
15. Guo, Y., D. Wang, and C. Tian, "Research on sensing matrix characteristics in microwave staring correlated imaging based on compressed sensing," Proceedings of 2014 IEEE International Conference on Imaging Systems and Techniques, 1-6, Island of Santorini, Greece, October 2014.
16. Bell, M. R., "Information theory and radar waveform design," IEEE Trans. on Information Theory, Vol. 9, 1578-1597, 1993. doi:10.1109/18.259642
17. Luo, Y., Z. Zhao, and C. Luo, "MIMO-OTHR waveform optimization based on the mutual information theory," Progress In Electromagnetics Research M, Vol. 46, 69-80, 2016. doi:10.2528/PIERM15102903
18. Tang, B., J. Tang, and Y. Peng, "MIMO radar waveform design in colored noise based on information theory," IEEE Transactions on Signal Processing, Vol. 58, No. 9, 4684-4697, 2010. doi:10.1109/TSP.2010.2050885
19. Maherin, I. and Q. Liang, "Radar sensor network for target detection using Chernoff information and relative entropy," Physical Communication, Vol. 13, 244-252, 2014. doi:10.1016/j.phycom.2014.01.003
20. Liu, W., Y. Lu, and M. Lesturgie, "Evolutionary algorithms based sparse spectrum waveform optimization," Principles of Waveform Diversity and Design, Vol. 2011, 152-162, 2011.
21. Mishra, A. and A. Shukla, "Mathematical analysis of schema survival for genetic algorithms having dual mutation," Soft Computing, Vol. 1, 1-9, 2011.
22. Boudamouz, B., P. Millot, and C. Pichot, "MIMO antenna design with genetic algorithm for TTW radar imaging," Proceedings of 2012 EuRAD 9th European Radar Conference, 150-153, Amsterdam, the Netherlands, October 2012.
23. Lellouch, G. and A. Mishra, "Multi-carrier based radar signal optimization using genetic algorithm," Advances in Intelligent Systems and Computing, Vol. 258, 525-534, 2014. doi:10.1007/978-81-322-1771-8_46
24. Liu, B. and D. Wang, "Orthogonal radiation field construction for microwave staring correlated imaging," Progress In Electromagnetics Research M, Vol. 57, 139-149, 2017. doi:10.2528/PIERM17042003
25. Cerf, R., "The quasispecies regime for the simple genetic algorithm with roulette wheel selection," Advances in Applied Probability, Vol. 49, No. 3, 903-926, 2017. doi:10.1017/apr.2017.26
26. Rubae, T., P. M. Meaney, P. Meincke, and K. D. Paulsen, "Nonlinear microwave imaging for breast-cancer screening using Gauss-Newton’s method and the CGLS inversion algorithm," IEEE Transactions on Antennas and Propagation, Vol. 55, 2320-2331, 2007.