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Hybrid System Radar Coincidence Imaging
Available online at www.sciencedirect.com
ScienceDirect Procedia Computer Science 107 (2017) 867 – 870
International Congress of Information and Communication Technology (ICICT 2017)
Hybrid System Radar Coincidence Imaging Yuchen He, Shitao Zhu*, Songlin Zhang, Mian Dong, Anxue Zhang and Zhuo Xu Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education & International Center for Dielectric Research, Xi’an Jiaotong University, Xi’an 710049, China * Corresponding author: [email protected] Tel.: +86 13679126046
Abstract A novel hybrid system radar coincidence imaging (HSRCI) is proposed in this paper. Radar coincidence imaging (RCI), as a novel kind of radar imaging system, has attracted widespread attention. RCI has the characteristics of high resolution and antiinterference. In addition, the RCI system can image not depend on the range-Doppler (RD) principle and simplify the receiver complexity. So it is considered as a good complement to synthetic aperture radar (SAR). However, the RCI system also has a lot of problems. Since RCI requires the transmit signal has the characteristics of time-space independence. Its pattern has anisotropy, so that the energy can not be gathered in the main lobe direction. This phenomenon will greatly affect the detection range. We propose a HSRCI system, its transmit signal consists of time-space independent signal and a fixed power signal together. HSRCI not only has the characteristics of RCI system. Simultaneously, it can also solve the problem of detection range. Simulation results demonstrate the effect of the HSRCI system. The HSRCI system has a positive effect on solving the problems of RCI. Keywords: hybrid system, radar coincidence imaging, time-space independent signal, detection range;
1. Introduction In recent years, ghost imaging (GI) aroused great concern. As a novel method of imaging, GI has many features that other imaging method are not available [1]. GI have two branches. One is the test branch which contains object, the object is irradiated and light intensity is collected by a bucket detector without resolution. The other one is the reference branch without object, using a CCD camera to scan the space. The object image can be obtained by making the test and the reference branch to do coincidence calculation. So people also called GI as coincidence imaging. After years of development, GI can be achieved by a variety of light sources. Entangled source, classical source or even microwave source [1]-[6]. Due to the high resolution and anti-interference characteristics of GI, people extend GI into the radar imaging field. For the existing radar imaging techniques. SAR and ISAR are the most widely used in this field. They are all dependent on the interaction between the radar and the target. Radar coincidence imaging (RCI) can stare imaging as
1877-0509 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 7th International Congress of Information and Communication Technology doi:10.1016/j.procs.2017.03.185
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Yuchen He et al. / Procedia Computer Science 107 (2017) 867 – 870
a complement to the SAR imaging system [7]-[10]. Simultaneously, RCI can not rely on Doppler method to image moving object. Motivated by classical coincidence imaging (CGI), RCI must build a random radiation source. Consequently, an array of N transmitting elements and a receiving element system settings are designed [7],[8]. With this arrangement, a time-space independent transmit signal can be generated. The synthetic pattern of the transmit signal has no apparent main lobe. Under these circumstances, the traditional radar system can have a better detection capability than RCI. In order to solve this problem of the RCI system, we put forward the HSRCI system. The core idea of HSRCI system is to superimpose the random signal and a fixed power signal. In this way, the total power of the system can be increased and the detection capability of the system can be improved. The HSRCI system is briefly described in the following section and is demonstrated through numerical simulations. 2. The HSRCI System To satisfy the need of building a random radiation source, the RCI system uses a number of transmitting subelements to construct a time-space independent transmit signal. Compared to RCI system, the most prominent characteristics of HSRCI system is adding a fixed power signal to the random signal. To some extent, HSRCI system is equivalent to the combination of traditional radar system and RCI system. The system diagram of RCI and HSRCI are shown below.
Fig. 1. (a) The RCI system schematic diagram;
(b) The HSRCI system schematic diagram.
The transmit signal of the HSRCI system can be expressed as St (t )
R(t ) S (t )
(1)
where denotes the transmit signal of the HSRCI system. denotes a random signal that complies with some distribution. denotes a signal having a certain fixed power. Simultaneously, the detection signal at the target wavefront is
ST r , t
M
¦ St t W m
m 1
(2)
Where represents the delay from transmit signal to the object and r represents any point within the object area. We set the receive signal of the HSRCI system as and the scattering coefficient of the object as . So we have the following relationship SR (t ) V r ST r , t
(3)
From the above formula to solve is a typical inverse problem. We can use convex optimization algorithm to
Yuchen He et al. / Procedia Computer Science 107 (2017) 867 – 870
869
optimize the process of solving this problem [11],[12] and eventually get the object image. 3. Simulation and Discussion In this section, the HSRCI system is simulated numerically. We conducted a series of comparative simulation. The results of image reconstruction of RCI system and HSRCI system were compared. We set a toy plane as the target. An iterative algorithm based on convex optimization is used to reconstruct the image.
Fig. 2. (a) Reconstruction result of RCI with 8000 times detection; (b) Reconstruction result of HSRCI with 8000 times detection; (c) Reconstruction result of HSRCI with 8000 times detection and multiple iterations; (d) Reconstruction result of HSRCI with 10000 times detection; (e) Reconstruction result of HSRCI with 12000 times detection; (f) Reconstruction result of HSRCI with 15000 times detection;
Figure 2 shows the simulation results. Fig. 2(a) represents the image reconstruction result of RCI. Fig. 2(b) represents the result of the HSRCI under same conditions. Contrast the two figures we can see that, RCI has a better result than HSRCI. However, due to the mixing of a fixed-power signal into a random signal. HSRCI has a better detection capability than RCI. The differences in image quality can be eliminated by other means. Fig. 2(c) shows the result that HSRCI achieves a similar result to RCI system by increasing the complexity of the algorithm. Simultaneously, we also can increase the number of detection to eliminate the differences of image quality. Fig. 2(d), Fig. 2(e) and Fig. 2(f) show that, with the increasing of number of detection, the results of reconstruction of the HSRCI system have been improved significantly. Finally, HSRCI system can achieve the same level of image reconstruction as the RCI system. Although adding a fixed power signal is equivalent to reducing the randomness of the system. The time-space independent signal can still been produced at the object wave-front to detect the object. We can use iterative algorithm based on convex optimization method to solve this inverse problem. The most critical is, we can adjust the number of detection or iteration to eliminate the influence of the transmit signal with fixed power on the image reconstruction of coincidence imaging. 4. Conclusion In order to overcome the problem of RCI system, we propose the HSRCI system to improve the detection range. On the basis of RCI system, we design a fixed power transmitting signal to combine with the random signal. Although such a method will affect the randomness of the system, thus affecting the final imaging results. We can adjust the number of detection and the algorithm complexity to eliminate the impact on image quality. Ultimately, we can improve the detection range of RCI system without affecting the imaging results. This technology has made active exploration to solve the problem of RCI system and to make RCI system more practical.
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Yuchen He et al. / Procedia Computer Science 107 (2017) 867 – 870
Acknowledgements This work was supported by National Natural Science Foundation of China (NSFC) (11404255), Doctoral Fund of Ministry of Education of China (20130201120013), 111 Project of China (B14040) and Fundamental Research Funds for the Central Universities. References 1. T. B. Pittman, Y. H. Shih, D. V. Strekalov, and A. V. Sergienko, Optical imaging by means of two-photon quantum entanglement, Phys. Rev. A, vol. 52, no. 5, pp. 3429–3432, 1995. 2. R. Roy and T. Kailath, Two-photon imaging with thermal light, Phys. Rev. Lett., vol. 94, no. 6, p. 063601, 2005. 3. A. Gatti, E. Brambilla, M. Bache, and L. A. Lugiato, Ghost imaging with thermal light: Comparing entanglement and classical correlation, Phys. Rev. Lett., vol. 93, no. 9, p. 093602, 2004. 4. G. Scarcelli, V. Berardi, and Y. Shih, Can two-photon correlation of chaotic light be considered as correlation of intensity fluctuations?, Phys.Rev. Lett., vol. 96, no. 6, p. 063602, 2006. 5. R. Meyers, K. S. Deacon, and Y. Shih, Ghost-imaging experiment by measuring reflected photons, Phys. Rev. A, vol. 77, no. 4, p. 041801(R), 2008. 6. J. H. Shapiro, Computational ghost imaging, Phys. Rev. A, vol. 78, no. 6, p. 061802(R), 2008. 7. D.Z. Li, X. Li, Y.L. Qin, Y.Q. Cheng, and H.Q. Wang, Radar Coincidence Imaging: An Instantaneous Imaging Technique With Stochastic Signals, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, 2014. 8. S. Zhu, A. Zhang, Z. Xu, and X. Dong, Radar Coincidence Imaging With Random Microwave Source, IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 1239-1242, 2015. 9. Y. Guo, X. He, and D. Wang, A novel super-resolution imaging method based on stochastic radiation radar array, Measurement Science and Technology, vol. 24, 2013. 10. C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, et al., Ghost imaging lidar via sparsity constraints, Applied Physics Letters, vol. 101, 2012. 11. A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM journal on imaging sciences, vol. 2, pp. 183-202,, 2009. 12. M. Z. A. Bhotto, M. O. Ahmad, and M. N. S. Swamy, An Improved Fast Iterative Shrinkage Thresholding Algorithm for Image Deblurring, SIAM journal on imaging sciences, vol. 8, pp. 1640-1657, 2015.
ScienceDirect Procedia Computer Science 107 (2017) 867 – 870
International Congress of Information and Communication Technology (ICICT 2017)
Hybrid System Radar Coincidence Imaging Yuchen He, Shitao Zhu*, Songlin Zhang, Mian Dong, Anxue Zhang and Zhuo Xu Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education & International Center for Dielectric Research, Xi’an Jiaotong University, Xi’an 710049, China * Corresponding author: [email protected] Tel.: +86 13679126046
Abstract A novel hybrid system radar coincidence imaging (HSRCI) is proposed in this paper. Radar coincidence imaging (RCI), as a novel kind of radar imaging system, has attracted widespread attention. RCI has the characteristics of high resolution and antiinterference. In addition, the RCI system can image not depend on the range-Doppler (RD) principle and simplify the receiver complexity. So it is considered as a good complement to synthetic aperture radar (SAR). However, the RCI system also has a lot of problems. Since RCI requires the transmit signal has the characteristics of time-space independence. Its pattern has anisotropy, so that the energy can not be gathered in the main lobe direction. This phenomenon will greatly affect the detection range. We propose a HSRCI system, its transmit signal consists of time-space independent signal and a fixed power signal together. HSRCI not only has the characteristics of RCI system. Simultaneously, it can also solve the problem of detection range. Simulation results demonstrate the effect of the HSRCI system. The HSRCI system has a positive effect on solving the problems of RCI. Keywords: hybrid system, radar coincidence imaging, time-space independent signal, detection range;
1. Introduction In recent years, ghost imaging (GI) aroused great concern. As a novel method of imaging, GI has many features that other imaging method are not available [1]. GI have two branches. One is the test branch which contains object, the object is irradiated and light intensity is collected by a bucket detector without resolution. The other one is the reference branch without object, using a CCD camera to scan the space. The object image can be obtained by making the test and the reference branch to do coincidence calculation. So people also called GI as coincidence imaging. After years of development, GI can be achieved by a variety of light sources. Entangled source, classical source or even microwave source [1]-[6]. Due to the high resolution and anti-interference characteristics of GI, people extend GI into the radar imaging field. For the existing radar imaging techniques. SAR and ISAR are the most widely used in this field. They are all dependent on the interaction between the radar and the target. Radar coincidence imaging (RCI) can stare imaging as
1877-0509 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 7th International Congress of Information and Communication Technology doi:10.1016/j.procs.2017.03.185
868
Yuchen He et al. / Procedia Computer Science 107 (2017) 867 – 870
a complement to the SAR imaging system [7]-[10]. Simultaneously, RCI can not rely on Doppler method to image moving object. Motivated by classical coincidence imaging (CGI), RCI must build a random radiation source. Consequently, an array of N transmitting elements and a receiving element system settings are designed [7],[8]. With this arrangement, a time-space independent transmit signal can be generated. The synthetic pattern of the transmit signal has no apparent main lobe. Under these circumstances, the traditional radar system can have a better detection capability than RCI. In order to solve this problem of the RCI system, we put forward the HSRCI system. The core idea of HSRCI system is to superimpose the random signal and a fixed power signal. In this way, the total power of the system can be increased and the detection capability of the system can be improved. The HSRCI system is briefly described in the following section and is demonstrated through numerical simulations. 2. The HSRCI System To satisfy the need of building a random radiation source, the RCI system uses a number of transmitting subelements to construct a time-space independent transmit signal. Compared to RCI system, the most prominent characteristics of HSRCI system is adding a fixed power signal to the random signal. To some extent, HSRCI system is equivalent to the combination of traditional radar system and RCI system. The system diagram of RCI and HSRCI are shown below.
Fig. 1. (a) The RCI system schematic diagram;
(b) The HSRCI system schematic diagram.
The transmit signal of the HSRCI system can be expressed as St (t )
R(t ) S (t )
(1)
where denotes the transmit signal of the HSRCI system. denotes a random signal that complies with some distribution. denotes a signal having a certain fixed power. Simultaneously, the detection signal at the target wavefront is
ST r , t
M
¦ St t W m
m 1
(2)
Where represents the delay from transmit signal to the object and r represents any point within the object area. We set the receive signal of the HSRCI system as and the scattering coefficient of the object as . So we have the following relationship SR (t ) V r ST r , t
(3)
From the above formula to solve is a typical inverse problem. We can use convex optimization algorithm to
Yuchen He et al. / Procedia Computer Science 107 (2017) 867 – 870
869
optimize the process of solving this problem [11],[12] and eventually get the object image. 3. Simulation and Discussion In this section, the HSRCI system is simulated numerically. We conducted a series of comparative simulation. The results of image reconstruction of RCI system and HSRCI system were compared. We set a toy plane as the target. An iterative algorithm based on convex optimization is used to reconstruct the image.
Fig. 2. (a) Reconstruction result of RCI with 8000 times detection; (b) Reconstruction result of HSRCI with 8000 times detection; (c) Reconstruction result of HSRCI with 8000 times detection and multiple iterations; (d) Reconstruction result of HSRCI with 10000 times detection; (e) Reconstruction result of HSRCI with 12000 times detection; (f) Reconstruction result of HSRCI with 15000 times detection;
Figure 2 shows the simulation results. Fig. 2(a) represents the image reconstruction result of RCI. Fig. 2(b) represents the result of the HSRCI under same conditions. Contrast the two figures we can see that, RCI has a better result than HSRCI. However, due to the mixing of a fixed-power signal into a random signal. HSRCI has a better detection capability than RCI. The differences in image quality can be eliminated by other means. Fig. 2(c) shows the result that HSRCI achieves a similar result to RCI system by increasing the complexity of the algorithm. Simultaneously, we also can increase the number of detection to eliminate the differences of image quality. Fig. 2(d), Fig. 2(e) and Fig. 2(f) show that, with the increasing of number of detection, the results of reconstruction of the HSRCI system have been improved significantly. Finally, HSRCI system can achieve the same level of image reconstruction as the RCI system. Although adding a fixed power signal is equivalent to reducing the randomness of the system. The time-space independent signal can still been produced at the object wave-front to detect the object. We can use iterative algorithm based on convex optimization method to solve this inverse problem. The most critical is, we can adjust the number of detection or iteration to eliminate the influence of the transmit signal with fixed power on the image reconstruction of coincidence imaging. 4. Conclusion In order to overcome the problem of RCI system, we propose the HSRCI system to improve the detection range. On the basis of RCI system, we design a fixed power transmitting signal to combine with the random signal. Although such a method will affect the randomness of the system, thus affecting the final imaging results. We can adjust the number of detection and the algorithm complexity to eliminate the impact on image quality. Ultimately, we can improve the detection range of RCI system without affecting the imaging results. This technology has made active exploration to solve the problem of RCI system and to make RCI system more practical.
870
Yuchen He et al. / Procedia Computer Science 107 (2017) 867 – 870
Acknowledgements This work was supported by National Natural Science Foundation of China (NSFC) (11404255), Doctoral Fund of Ministry of Education of China (20130201120013), 111 Project of China (B14040) and Fundamental Research Funds for the Central Universities. References 1. T. B. Pittman, Y. H. Shih, D. V. Strekalov, and A. V. Sergienko, Optical imaging by means of two-photon quantum entanglement, Phys. Rev. A, vol. 52, no. 5, pp. 3429–3432, 1995. 2. R. Roy and T. Kailath, Two-photon imaging with thermal light, Phys. Rev. Lett., vol. 94, no. 6, p. 063601, 2005. 3. A. Gatti, E. Brambilla, M. Bache, and L. A. Lugiato, Ghost imaging with thermal light: Comparing entanglement and classical correlation, Phys. Rev. Lett., vol. 93, no. 9, p. 093602, 2004. 4. G. Scarcelli, V. Berardi, and Y. Shih, Can two-photon correlation of chaotic light be considered as correlation of intensity fluctuations?, Phys.Rev. Lett., vol. 96, no. 6, p. 063602, 2006. 5. R. Meyers, K. S. Deacon, and Y. Shih, Ghost-imaging experiment by measuring reflected photons, Phys. Rev. A, vol. 77, no. 4, p. 041801(R), 2008. 6. J. H. Shapiro, Computational ghost imaging, Phys. Rev. A, vol. 78, no. 6, p. 061802(R), 2008. 7. D.Z. Li, X. Li, Y.L. Qin, Y.Q. Cheng, and H.Q. Wang, Radar Coincidence Imaging: An Instantaneous Imaging Technique With Stochastic Signals, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, 2014. 8. S. Zhu, A. Zhang, Z. Xu, and X. Dong, Radar Coincidence Imaging With Random Microwave Source, IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 1239-1242, 2015. 9. Y. Guo, X. He, and D. Wang, A novel super-resolution imaging method based on stochastic radiation radar array, Measurement Science and Technology, vol. 24, 2013. 10. C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, et al., Ghost imaging lidar via sparsity constraints, Applied Physics Letters, vol. 101, 2012. 11. A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM journal on imaging sciences, vol. 2, pp. 183-202,, 2009. 12. M. Z. A. Bhotto, M. O. Ahmad, and M. N. S. Swamy, An Improved Fast Iterative Shrinkage Thresholding Algorithm for Image Deblurring, SIAM journal on imaging sciences, vol. 8, pp. 1640-1657, 2015.