Analysis of Quantum Radar Cross Section of Curved Surface Target

Kun, 陈坤 Chen; Shuxin, 陈树新 Chen; Dewei, 吴德伟 Wu; Xi, 王希 Wang; Mi, 史密 Shi

doi:10.3788/aos201636.1227002

Cited by 8 publications

(1 citation statement)

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“…They speculated that this new macroscopic quantum phenomenon can be applied in the detection and identification of stealth weapons and the field of biomedicine. Chen et al [14] investigated the single-photon and two-photon QRCS of a single surface based on the cylindrical model. Significant indepth studies on 2D targets have been undertaken, building a mature theoretical and numerical foundation for the above research, but QRCS for 3D targets mostly focuses on the numerical calculation of simple targets.…”

Section: Introductionmentioning

confidence: 99%

Study on the Comprehensive Optimization of Quantum Radar Stealth Based on the Waverider Warhead

Zhang

Huang

et al. 2023

Aerospace

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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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Section: Introductionmentioning

confidence: 99%