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Статті в журналах з теми "Quantum radar"

Автор: Grafiati

Опубліковано: 13 грудня 2023

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1

Luong, David, Sreeraman Rajan, and Bhashyam Balaji. "Quantum Monopulse Radar." Applied Computational Electromagnetics Society 35, no. 11 (February 5, 2021): 1430–32. http://dx.doi.org/10.47037/2020.aces.j.351184.

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Анотація:

We evaluate the feasibility of a quantum monopulse radar, focusing on quantum illumination (QI) radars and quantum two-mode squeezing (QTMS) radars. Based on their similarity with noise radar, for which monopulse operation is known to be possible, we find that QTMS radars can be adapted into monopulse radars, but QI radars cannot. We conclude that quantum monopulse radars are feasible.

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2

Djordjevic, Ivan B. "On Entanglement-Assisted Multistatic Radar Techniques." Entropy 24, no. 7 (July 17, 2022): 990. http://dx.doi.org/10.3390/e24070990.

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Анотація:

Entanglement-based quantum sensors have much better sensitivity than corresponding classical sensors in a noisy and lossy regime. In our recent paper, we showed that the entanglement-assisted (EA) joint monostatic–bistatic quantum radar performs much better than conventional radars. Here, we propose an entanglement-assisted (EA) multistatic radar that significantly outperforms EA bistatic, coherent state-based quantum, and classical radars. The proposed EA multistatic radar employs multiple entangled transmitters performing transmit-side optical phase conjugation, multiple coherent detection-based receivers serving as EA detectors, and a joint detector.

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3

Lanzagorta, Marco. "Quantum Radar." Synthesis Lectures on Quantum Computing 3, no. 1 (October 31, 2011): 1–139. http://dx.doi.org/10.2200/s00384ed1v01y201110qmc005.

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4

Djordjevic, Ivan B. "Entanglement-Assisted Joint Monostatic-Bistatic Radars." Entropy 24, no. 6 (May 26, 2022): 756. http://dx.doi.org/10.3390/e24060756.

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Анотація:

With the help of entanglement, we can build quantum sensors with sensitivity better than that of classical sensors. In this paper we propose an entanglement assisted (EA) joint monostatic-bistatic quantum radar scheme, which significantly outperforms corresponding conventional radars. The proposed joint monostatic-bistatic quantum radar is composed of two radars, one having both wideband entangled source and EA detector, and the second one with only an EA detector. The optical phase conjugation (OPC) is applied on the transmitter side, while classical coherent detection schemes are applied in both receivers. The joint monostatic-bistatic integrated EA transmitter is proposed suitable for implementation in LiNbO3 technology. The detection probability of the proposed EA joint target detection scheme outperforms significantly corresponding classical, coherent states-based quantum detection, and EA monostatic detection schemes. The proposed EA joint target detection scheme is evaluated by modelling the direct radar return and forward scattering channels as both lossy and noisy Bosonic channels, and assuming that the distribution of entanglement over idler channels is not perfect.

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5

Norouzi, Milad, Jamileh Seyed-Yazdi, Seyed Mohammad Hosseiny, and Patrizia Livreri. "Investigation of the JPA-Bandwidth Improvement in the Performance of the QTMS Radar." Entropy 25, no. 10 (September 22, 2023): 1368. http://dx.doi.org/10.3390/e25101368.

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Анотація:

Josephson parametric amplifier (JPA) engineering is a significant component in the quantum two-mode squeezed radar (QTMS) to enhance, for instance, radar performance and the detection range or bandwidth. We simulated a proposal of using engineered JPA (EJPA) to enhance the performance of a QTMS radar. We defined the signal-to-noise ratio (SNR) and detection range equations of the QTMS radar. The engineered JPA led to a remarkable improvement in the quantum radar performance, i.e., a large enhancement in SNR of about 6 dB more than the conventional QTMS radar (with respect to the latest version of the QTMS radar and not to the classical radar), a substantial improvement in the probability of detection through far fewer channels. The important point in this work was that we expressed the importance of choosing suitable detectors for the QTMS radars. Finally, we simulated the transmission of the signal to the target in the QTMS radar and obtained a huge increase in the QTMS radar range, up to 482 m in the current study.

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6

Lu, Shaoze, Zhijun Meng, Jun Huang, Mingxu Yi, and Zeyang Wang. "Study on Quantum Radar Detection Probability Based on Flying-Wing Stealth Aircraft." Sensors 22, no. 16 (August 9, 2022): 5944. http://dx.doi.org/10.3390/s22165944.

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Анотація:

The development of quantum radar technology presents a challenge to stealth targets, so it is necessary to study the quantum detection probability. In this study, an analytical expression of the quantum radar cross section (QRCS) for complex targets is presented. Based on this QRCS expression, a calculation method for the detection probability for quantum radar is creatively proposed. Moreover, a self-designed flying-wing stealth aircraft is adopted to obtain the detection probability distributions of the conventional radar and the quantum radar in different directions. As revealed by the result of this study, the detection probabilities of the quantum radar and the conventional radar are significantly different, and the detection probability of the quantum radar has obvious advantages in most regions with a certain distance.

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7

Tian, Zhi-Fu, Di Wu, and Tao Hu. "Theoretical study of single-photon quantum radar cross-section of cylindrical curved surface." Acta Physica Sinica 71, no. 3 (2022): 034204. http://dx.doi.org/10.7498/aps.71.20211295.

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Анотація:

To examine the single-photon quantum radar cross-section of cylindrical surface and its specific advantages over the classical radar cross-section, a photon wave function in which the distance vectors causing interference are decomposed is introduced in this study. A closed-form expression of the single-photon quantum radar cross-section of cylindrical surface is derived. The influences of the length and curvature radius of cylindrical surfaces with different electrical sizes are analyzed, and the closed-form expressions of the quantum and classical radar cross-sections of cylindrical surface are compared with each other. The analyses of the closed-form expression and simulation results show that the electrical length of the cylindrical surface determines the number of side lobes of the quantum radar cross-section; meanwhile, the curvature radius has a linear relation with the overall strength of the quantum radar cross-section, and the electrical size of the curvature radius determines the envelope of the quantum radar cross-section curve. Compared with the classical radar cross-section, the quantum radar cross-section of a cylindrical surface has the advantage of side-lobe enhancement, which is beneficial for detecting stealth targets.

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8

Chang, C. W. Sandbo, A. M. Vadiraj, J. Bourassa, B. Balaji, and C. M. Wilson. "Quantum-enhanced noise radar." Applied Physics Letters 114, no. 11 (March 18, 2019): 112601. http://dx.doi.org/10.1063/1.5085002.

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9

Blakely, Jonathan N. "Bounds on Probability of Detection Error in Quantum-Enhanced Noise Radar." Quantum Reports 2, no. 3 (July 21, 2020): 400–413. http://dx.doi.org/10.3390/quantum2030028.

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Анотація:

Several methods for exploiting quantum effects in radar have been proposed, and some have been shown theoretically to outperform any classical radar scheme. Here, a model is presented of quantum-enhanced noise radar enabling a similar analysis. This quantum radar scheme has a potential advantage in terms of ease of implementation insofar as it requires no quantum memory. A significant feature of the model introduced is the inclusion of quantum noise consistent with the Heisenberg uncertainty principle applied to simultaneous determination of field quadratures. The model enables direct comparison to other quantum and classical radar schemes. A bound on the probability of an error in target detection is shown to match that of the optimal classical-state scheme. The detection error is found to be typically higher than for ideal quantum illumination, but orders of magnitude lower than for the most similar classical noise radar scheme.

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10

Kulshreshtha, Abhijit, and Abdulkareem Sh Mahdi Al-Obaidi. "Stealth Detection System via Multistage Radar and Quantum Radar." Indonesian Journal of Science and Technology 5, no. 3 (December 1, 2020): 470–86. http://dx.doi.org/10.17509/ijost.v5i3.26806.

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Анотація:

In today’s era of advanced weapons and technology development, many remarkable inventions have shifted the balance of war towards the strategically enhanced military equipped with tactical weapons and armaments. One of these strategic advancements is stealth technology due to which stealth aircraft are high in demand for the military. The question that rises is How to detect a stealth object? This paper proposes a novel anti-stealth technique using void detection, high frequency wave interference and neutrino beam propagation. Void detection method uses a modified satellite-based radar that searches for areas in the aerospace from which the transmitted signals sent to the ground receiving station are blocked or deflected. High frequency wave interference method is used to generate a stellar trajectory of the stealth aircraft at the detected void. Neutrino beam comprises of energy quanta mainly neutrinos, which are able to surpass the absorption or deflection systems in the stealth body of aircraft. This unique phenomenon produces a moving image, which is the precise location of the aircraft in the space. Using these methods, the trajectory of the aircraft is detected which ultimately leads to the detection of the stealth aircraft itself. The newly proposed methods which are theoretically more reliable than the existing methods may not have been tested but the method planning make them practically feasible considering that the technology used is a part of advanced engineering today.

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11

Lukin, Konstantin. "Evolution of Quantum Radar Concept to Noise Radar Concept." IEEE Aerospace and Electronic Systems Magazine 35, no. 11 (November 1, 2020): 30–36. http://dx.doi.org/10.1109/maes.2020.3004015.

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12

Salmanogli, Ahmad, Dincer Gokcen, and H. Selcuk Gecim. "Entanglement Sustainability in Quantum Radar." IEEE Journal of Selected Topics in Quantum Electronics 26, no. 6 (November 2020): 1–11. http://dx.doi.org/10.1109/jstqe.2020.3020620.

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13

Daum, Fred. "Three Special Sessions on Quantum Radar at IEEE Radar Conferences." IEEE Aerospace and Electronic Systems Magazine 36, no. 5 (May 1, 2021): 61–63. http://dx.doi.org/10.1109/maes.2021.3056703.

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14

Daum, Fred. "Quantum Radar Cost and Practical Issues." IEEE Aerospace and Electronic Systems Magazine 35, no. 11 (November 1, 2020): 8–20. http://dx.doi.org/10.1109/maes.2020.2982755.

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15

Lanzagorta, Marco, and Jeffrey Uhlmann. "Opportunities and Challenges of Quantum Radar." IEEE Aerospace and Electronic Systems Magazine 35, no. 11 (November 1, 2020): 38–56. http://dx.doi.org/10.1109/maes.2020.3004053.

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16

Zhao, Sheng-Zhi. "Quantum detection theory and optimum strategy in quantum radar system." Journal of Engineering 2019, no. 21 (November 1, 2019): 7428–31. http://dx.doi.org/10.1049/joe.2019.0631.

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17

Potapov, Alexander A. "Mathematical Foundations of the Fractal Scaling Method in Statistical Radiophysics and Applications." Radioelectronics. Nanosystems. Information Technologies. 13, no. 3 (September 30, 2021): 245–96. http://dx.doi.org/10.17725/rensit.2021.13.245.

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Анотація:

The system of basic mathematical concepts and constructions underlying the modern global fractal-scaling method developed by the author is presented. An overview of the main results on the creation of new information technologies based on textures, fractals (multifractals), fractional operators, scaling effects and nonlinear dynamics methods obtained by the author and his students for more than 40 years (from 1979 to the present) at the V.A. Kotelnikov Institute of Radioengineering and Electronics of RAS. It is shown that, for the first time in the world, new dimensional and topological (and not energy!) Features or invariants were proposed and then effectively applied for problems in radio physics and radio electronics, which are combined under the generalized concept of "sample topology" ~ "fractal signature". The author discovered, proposed and substantiated a new type and new method of modern radar, namely, fractal-scaling or scale-invariant radar. It should be noted that fractal radars are, in fact, a necessary intermediate stage on the path of transition to cognitive radar and quantum radar.

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18

Cho, Adrian. "The short, strange life of quantum radar." Science 369, no. 6511 (September 24, 2020): 1556–57. http://dx.doi.org/10.1126/science.369.6511.1556.

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19

Jeong, Jong-Jin, Sang-Woon Jeon, Bang-Chul Jung, Choul-Young Kim, and Jin-Woong Kim. "Tendencies and Prospects on Quantum Radar Systems." Journal of Korean Institute of Communications and Information Sciences 43, no. 12 (December 31, 2018): 2155–67. http://dx.doi.org/10.7840/kics.2018.43.12.2155.

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20

Balaji, Bhashyam, Marco Frasca, and Alfonso Farina. "Quantum Radar Research: A Snapshot in Time." IEEE Aerospace and Electronic Systems Magazine 35, no. 4 (April 1, 2020): 74–76. http://dx.doi.org/10.1109/maes.2020.2977800.

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21

McKenna, Phil. "Quantum trickery could lead to stealth radar." New Scientist 209, no. 2805 (March 2011): 28. http://dx.doi.org/10.1016/s0262-4079(11)60672-6.

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22

Hambling, David. "Quantum radar can even spot stealth planes." New Scientist 240, no. 3204 (November 2018): 10. http://dx.doi.org/10.1016/s0262-4079(18)32110-9.

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23

Aron, Jacob. "Warning speedsters: you can't evade quantum radar." New Scientist 216, no. 2896-2897 (December 2012): 9. http://dx.doi.org/10.1016/s0262-4079(12)63219-9.

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24

Peshko, I., D. Mogilevtsev, I. Karuseichyk, A. Mikhalychev, A. P. Nizovtsev, G. Ya Slepyan, and A. Boag. "Quantum noise radar: superresolution with quantum antennas by accessing spatiotemporal correlations." Optics Express 27, no. 20 (September 26, 2019): 29217. http://dx.doi.org/10.1364/oe.27.029217.

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25

Lu, Shaoze, Zhijun Meng, Jun Huang, and Mingxu Yi. "Study on the Comprehensive Optimization of Quantum Radar Stealth Based on the Waverider Warhead." Aerospace 10, no. 7 (June 30, 2023): 602. http://dx.doi.org/10.3390/aerospace10070602.

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Анотація:

Quantum radar is a novel detection method that combines radar and quantum technologies. It exceeds the detection threshold and poses a threat to conventional stealth targets. This work aims to derive the expression of the quantum radar cross-section of a new complex target. The calculation formula of QRCS was derived after introducing the relative photon parameters and vector dot product. Subsequently, a comprehensive optimization model of quantum stealth and lift–drag ratio based on a genetic algorithm was proposed for the waverider warhead. During the optimization process, we proposed an optimization method with the objective function of the QRCS pioneering design value and achieved better outcomes than the optimization method using the average value in terms of QRCS performance and lift–drag ratio in the important azimuths of the waverider. By changing the design variables of the waverider warhead and using this new optimization method, the QRCS of the waverider in the forward and lateral angles were minimized, remarkably improving the aerodynamic performance of the waverider. Similarly, the optimization results show that the proposed design value optimization method is feasible.

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26

Luong, David, Bhashyam Balaji, and Sreeraman Rajan. "Quantum Two-Mode Squeezing Radar and Noise Radar: Correlation Coefficient and Integration Time." IEEE Access 8 (2020): 185544–47. http://dx.doi.org/10.1109/access.2020.3029473.

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27

Luong, David, and Bhashyam Balaji. "Quantum two‐mode squeezing radar and noise radar: covariance matrices for signal processing." IET Radar, Sonar & Navigation 14, no. 1 (January 2020): 97–104. http://dx.doi.org/10.1049/iet-rsn.2019.0090.

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28

Luong, David, Sreeraman Rajan, and Bhashyam Balaji. "Quantum Two-Mode Squeezing Radar and Noise Radar: Correlation Coefficients for Target Detection." IEEE Sensors Journal 20, no. 10 (May 15, 2020): 5221–28. http://dx.doi.org/10.1109/jsen.2020.2971851.

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29

Barzanjeh, S., S. Pirandola, D. Vitali, and J. M. Fink. "Microwave quantum illumination using a digital receiver." Science Advances 6, no. 19 (May 2020): eabb0451. http://dx.doi.org/10.1126/sciadv.abb0451.

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Анотація:

Quantum illumination uses entangled signal-idler photon pairs to boost the detection efficiency of low-reflectivity objects in environments with bright thermal noise. Its advantage is particularly evident at low signal powers, a promising feature for applications such as noninvasive biomedical scanning or low-power short-range radar. Here, we experimentally investigate the concept of quantum illumination at microwave frequencies. We generate entangled fields to illuminate a room-temperature object at a distance of 1 m in a free-space detection setup. We implement a digital phase-conjugate receiver based on linear quadrature measurements that outperforms a symmetric classical noise radar in the same conditions, despite the entanglement-breaking signal path. Starting from experimental data, we also simulate the case of perfect idler photon number detection, which results in a quantum advantage compared with the relative classical benchmark. Our results highlight the opportunities and challenges in the way toward a first room-temperature application of microwave quantum circuits.

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30

Wang Shu, Ren Yi-Chong, Rao Rui-Zhong, and Miao Xi-Kui. "Influence of atmosphere attenuation on quantum interferometric radar." Acta Physica Sinica 66, no. 15 (2017): 150301. http://dx.doi.org/10.7498/aps.66.150301.

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31

Luong, David, Sreeraman Rajan, and Bhashyam Balaji. "Entanglement-Based Quantum Radar: From Myth to Reality." IEEE Aerospace and Electronic Systems Magazine 35, no. 4 (April 1, 2020): 22–35. http://dx.doi.org/10.1109/maes.2020.2970261.

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32

Frasca, Marco, Alfonso Farina, and Bhashyam Balaji. "Foreword to the Special Issue on Quantum Radar." IEEE Aerospace and Electronic Systems Magazine 35, no. 4 (April 1, 2020): 4–7. http://dx.doi.org/10.1109/maes.2020.2977851.

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33

Bourassa, Jerome, and Christopher M. Wilson. "Progress Toward an All-Microwave Quantum Illumination Radar." IEEE Aerospace and Electronic Systems Magazine 35, no. 11 (November 1, 2020): 58–69. http://dx.doi.org/10.1109/maes.2020.3024422.

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34

Liu, Kang, Huai-Tie Xiao, and Hong-Qi Fan. "Analysis and Simulation of Quantum Radar Cross Section." Chinese Physics Letters 31, no. 3 (March 2014): 034202. http://dx.doi.org/10.1088/0256-307x/31/3/034202.

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35

Bowell, Rory A., Matthew J. Brandsema, Ram M. Narayanan, Stephen W. Howell, and Jonathan M. Dilger. "Tripartite Correlations in Quantum Radar and Communication Systems." Progress In Electromagnetics Research M 115 (2023): 83–92. http://dx.doi.org/10.2528/pierm23011003.

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36

Salmanogli, Ahmad, and Dincer Gokcen. "Analysis of Quantum Radar Cross-Section by Canonical Quantization Method (Full Quantum Theory)." IEEE Access 8 (2020): 205487–94. http://dx.doi.org/10.1109/access.2020.3037364.

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37

Borderieux, Sylvain, Arnaud Coatanhay, and Ali Khenchaf. "Quantum Illumination Radar Using Polarization States of Photons in Atmosphere: Quantum Information Approach." Progress In Electromagnetics Research B 103 (2023): 101–18. http://dx.doi.org/10.2528/pierb23051804.

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38

Wang, Yifan. "State-of-art applications and the function of quantum entanglement in quantum information." Theoretical and Natural Science 10, no. 1 (November 17, 2023): 9–15. http://dx.doi.org/10.54254/2753-8818/10/20230302.

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Анотація:

Quantum information is a cutting-edge technology that has numerous applications. It mainly makes usage of some quantum entanglement characteristics and uses the quantum entangled state as a carrier for information transfer. Therefore, compared to traditional information, quantum information has excellent features, e.g., stronger security and reduced susceptibility to interference. This article introduces the definition, concept, characteristics and history of quantum entanglement and quantum information. To be specific, this study lists the applications of quantum entanglement in communication and radar. In addition, it gives an outlook on the future function of quantum entanglement in quantum information based on the advantages and disadvantages of quantum entanglement. Contemporarily, the field of physics is rapidly advancing in both quantum entanglement and quantum information, and there have also been significant technological advancements. In experiments, scientists have been able to extend the transmission distance of quantum information to great distances. At the same time, scholars are looking for ways to minimise the interference of quantum information during transmission. In constant exploration and experimentation, the experimental results have inspired scientists to explore the deeper realms of quantum information.

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39

XU Ze-hua, 徐泽华, 李伟 LI Wei, 许强 XU Qiang, and 郑家毅 ZHENG Jia-yi. "Analysis of Quantum Radar Cross Section of Conical Composite Target." ACTA PHOTONICA SINICA 47, no. 4 (2018): 429001. http://dx.doi.org/10.3788/gzxb20184704.0429001.

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40

Tian, Zhifu, Di Wu, and Tao Hu. "Analysis of Quantum Radar Cross-Section of Dihedral Corner Reflector." IEEE Photonics Technology Letters 33, no. 22 (November 15, 2021): 1250–53. http://dx.doi.org/10.1109/lpt.2021.3116055.

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41

Chen Kun, 陈坤, 陈树新 Chen Shuxin, 吴德伟 Wu Dewei, 王希 Wang Xi, and 史密 Shi Mi. "Analysis of Quantum Radar Cross Section of Curved Surface Target." Acta Optica Sinica 36, no. 12 (2016): 1227002. http://dx.doi.org/10.3788/aos201636.1227002.

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42

Frasca, Marco, Alfonso Farina, and Bhashyam Balaji. "Foreword to the Special Issue on Quantum Radar—Part 2." IEEE Aerospace and Electronic Systems Magazine 35, no. 11 (November 1, 2020): 4–7. http://dx.doi.org/10.1109/maes.2020.3022439.

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43

Liu, Tianqu, Jinping Sun, Guohua Wang, and Yilong Lu. "A Multi-Objective Quantum Genetic Algorithm for MIMO Radar Waveform Design." Remote Sensing 14, no. 10 (May 16, 2022): 2387. http://dx.doi.org/10.3390/rs14102387.

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Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (PCCR) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (PASR) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the PASR and PCCR. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the PASR and PCCR metrics of the waveform set are 0.95–3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize PASR and PCCR of the MIMO radar waveform set simultaneously.

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44

KHETCHIKOV, D. M., B. B. PANKOV, S. N. BULICHEV, and A. V. MALKUTA. "A METHOD OF RADAR STATION ALIGNMENT BASED ON REFERENCE DATA OF THE INTERNATIONAL LASER LOCATION SERVICE." Fundamental and Applied Problems of Engineering and Technology 2 (2021): 178–82. http://dx.doi.org/10.33979/2073-7408-2021-346-2-178-182.

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The article describes the method of adjusting a ground-based radar station based on reference information received from the quantum-optical devices of the International laser location service and provides calculated relations for processing data of alignment measurements…

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45

Qi, Cheng, Junwei Xie, Haowei Zhang, Zihang Ding, and Xiao Yang. "Optimal Configuration of Array Elements for Hybrid Distributed PA-MIMO Radar System Based on Target Detection." Remote Sensing 14, no. 17 (August 23, 2022): 4129. http://dx.doi.org/10.3390/rs14174129.

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Анотація:

This paper establishes a hybrid distributed phased array multiple-input multiple-output (PA-MIMO) radar system model to improve the target detection performance by combining coherent processing gain and spatial diversity gain. First, the radar system signal model and array space configuration model for the PA-MIMO radar are established. Then, a novel likelihood ratio test (LRT) detector is derived based on the Neyman–Pearson (NP) criterion in a fixed noise background. It can jointly optimize the coherent processing gain and spatial diversity gain of the system by implementing subarray level and array element level optimal configuration at both receiver and transmitter ends in a uniform blocking manner. On this basis, three typical optimization problems are discussed from three aspects, i.e., the detection probability, the effective radar range, and the radar system equipment volume. The approximate closed-form solutions of them are constructed and solved by the proposed quantum particle swarm optimization-based stochastic rounding (SR-QPSO) algorithm. Through the simulations, it is verified that the proposed optimal configuration of the hybrid distributed PA-MIMO radar system offers substantial improvements compared to the other typical radar systems, detection probability of 0.98, and an effective range of 1166.3 km, which significantly improves the detection performance.

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46

Zhao, Feixiang, Yongxiang Liu, Kai Huo, and Zhongshuai Zhang. "Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum-Behaved Particle Swarm Optimization." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/7273061.

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Анотація:

A novel evolutionary extreme learning machine (ELM) based on improved quantum-behaved particle swarm optimization (IQPSO) for radar target classification is presented in this paper. Quantum-behaved particle swarm optimization (QPSO) has been used in ELM to solve the problem that ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. But the method for calculating the characteristic length of Delta potential well of QPSO may reduce the global search ability of the algorithm. To solve this issue, a new method to calculate the characteristic length of Delta potential well is proposed in this paper. Experimental results based on the benchmark functions validate the better performance of IQPSO against QPSO in most cases. The novel algorithm is also evaluated by using real-world datasets and radar data; the experimental results indicate that the proposed algorithm is more effective than BP, SVM, ELM, QPSO-ELM, and so on, in terms of real-time performance and accuracy.

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47

LI Xu, 李旭, 聂敏 NIE Min, 杨光 YANG Guang, and 裴昌幸 PEI Chang-xing. "The Strategy and Performance Simulation of Quantum Entangled Radar′s Survivability." ACTA PHOTONICA SINICA 44, no. 11 (2015): 1127002. http://dx.doi.org/10.3788/gzxb20154411.1127002.

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48

Ren Yi-Chong, Wang Shu, Rao Rui-Zhong, and Miao Xi-Kui. "Influence of atmospheric scintillation on entangled coherent states quantum interferometric radar." Acta Physica Sinica 67, no. 14 (2018): 140301. http://dx.doi.org/10.7498/aps.67.20172401.

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49

Luong, David, C. W. Sandbo Chang, A. M. Vadiraj, Anthony Damini, Christopher M. Wilson, and Bhashyam Balaji. "Receiver Operating Characteristics for a Prototype Quantum Two-Mode Squeezing Radar." IEEE Transactions on Aerospace and Electronic Systems 56, no. 3 (June 2020): 2041–60. http://dx.doi.org/10.1109/taes.2019.2951213.

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50

Brandsema, Matthew J., Ram M. Narayanan, and Marco Lanzagorta. "The Effect of Polarization on the Quantum Radar Cross Section Response." IEEE Journal of Quantum Electronics 53, no. 2 (April 2017): 1–9. http://dx.doi.org/10.1109/jqe.2017.2657321.

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