Study on Quantum Radar Detection Probability Based on Flying-Wing Stealth Aircraft

Lu, Shaoze; Zhang, Meng; Huang, Jun; Yi, Mingxu; Wang, Zeyang

doi:10.3390/s22165944

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…There is limited literature available on quantum radars currently. A study on quantum radars was carried out in [255]. The developments and design considerations were covered in this study.…”

Section: Quantum Radarsmentioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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<p> The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are also provided. </p>

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Section: Quantum Radarsmentioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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“…Similar to the characteristics of the potential field, the probability of radar detection increases as the UCAV approaches radars [6]. Furthermore, if multiple radars overlap, a distinct calculation method is utilized based on the multi-radar fusion criteria [7]. (3) Spatial configuration effect of the Radar Cross Section (RCS).…”

Section: Introductionmentioning

confidence: 99%

Memory-Enhanced Twin Delayed Deep Deterministic Policy Gradient (ME-TD3)-Based Unmanned Combat Aerial Vehicle Trajectory Planning for Avoiding Radar Detection Threats in Dynamic and Unknown Environments

Li,

Zhang,

Liu

2023

Remote Sensing

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Unmanned combat aerial vehicle (UCAV) trajectory planning to avoid radar detection threats is a complicated optimization problem that has been widely studied. The rapid changes in Radar Cross Sections (RCSs), the unknown cruise trajectory of airborne radar, and the uncertain distribution of radars exacerbate the complexity of this problem. In this paper, we propose a novel UCAV trajectory planning method based on deep reinforcement learning (DRL) technology to overcome the adverse impacts caused by the dynamics and randomness of environments. A predictive control model is constructed to describe the dynamic characteristics of the UCAV trajectory planning problem in detail. To improve the UCAV’s predictive ability, we propose a memory-enhanced twin delayed deep deterministic policy gradient (ME-TD3) algorithm that uses an attention mechanism to effectively extract environmental patterns from historical information. The simulation results show that the proposed method can successfully train UCAVs to carry out trajectory planning tasks in dynamic and unknown environments. Furthermore, the ME-TD3 algorithm outperforms other classical DRL algorithms in UCAV trajectory planning, exhibiting superior performance and adaptability.

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“…Consequently, researchers have introduced CNNs into these fields where the CNN also obtains leading performance. Moreover, one of the active research areas on jamming is related to jamming of the classical and quantum radar [12]. Researchers used ResNet-AE [13] or Quantumsecured imaging [14] to recognize the jamming of radar.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Multi-Node Jamming Recognition Method Based on Deep Residual Network

Shen

Zhu

et al. 2022

Electronics

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Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6–14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3–7% and 5–12%, respectively, under low JSR conditions compared with the existing single-node method.

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Study on Quantum Radar Detection Probability Based on Flying-Wing Stealth Aircraft

Cited by 6 publications

References 13 publications

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Memory-Enhanced Twin Delayed Deep Deterministic Policy Gradient (ME-TD3)-Based Unmanned Combat Aerial Vehicle Trajectory Planning for Avoiding Radar Detection Threats in Dynamic and Unknown Environments

Cooperative Multi-Node Jamming Recognition Method Based on Deep Residual Network

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