Effect of Time-Varying Plasma Sheath on Hypersonic Vehicle-Borne Radar Target Detection

Song, Lihao; Li, Xiaoping; Liu, Yanming

doi:10.1109/jsen.2021.3077727

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…In the research of seal cutting robots, reinforcement learning can help robots learn how to select the best seal cutting tools and forces based on the input seal cutting patterns and requirements, in order to achieve the expected results [20] . By using object detection algorithms, robots can monitor the position and shape of seals in real-time, and make adjustments and corrections as needed to ensure the accuracy and consistency of seal engraving [21] . The feature fusion classification network also plays an important role in the research of seal engraving robots.…”

Section: Technology For Seal Recognitionmentioning

confidence: 99%

Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence

Tan,

Khayril Anwar Bin Khairudin

2024

JAIT

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The rapid development of robotics technology has made people's lives and work more convenient and efficient. The research and simulation of robots combined with reinforcement learning intelligent algorithms have become a hotspot in various fields of robot applications. In view of this, this study is based on deep reinforcement learning convolutional neural networks, combined with point cloud models, proximal strategy optimization algorithms, and flexible action evaluation algorithms. A seal cutting robot based on deep reinforcement learning has been proposed. The final results show that the descent speed of the seal cutting robot with the root mean square difference as the performance standard is about 1% faster than the flexible action evaluation algorithm. About 2% faster than the proximal strategy optimization algorithm. It is about 4% faster than the deep deterministic strategy gradient algorithm. This indicates that the research model has certain advantages in terms of actual accuracy after cutting. The fluctuation of this model is about 10% smaller than the evaluation of flexible actions and about 60% smaller than the gradient of deep deterministic strategies. Therefore, the research model has the highest overall stability without falling into local optima. In addition, compared to the near end strategy optimization algorithm, it falls into local optima, resulting in a low coincidence degree of about 17%. The deep deterministic strategy gradient algorithm has a large fluctuation amplitude during the seal cutting process, and the overall curve is relatively slow, with a final overlap of about 70%. The overlap degree of flexible action evaluation is slightly higher by about 83%. The maximum stability of the model's overlap is best around 90%. Through experiments, it can be found that the seal cutting robot proposed in the study based on deep reinforcement learning maintains certain advantages in performance indicators in various types of tests.

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Section: Technology For Seal Recognitionmentioning

confidence: 99%

Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence

Tan,

Khayril Anwar Bin Khairudin

2024

JAIT

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“…For CF-SAR with drastic friction with atmosphere, the nonuniform and time-varying plasma sheath will be generated due to the heating up of the radar surface [11]. Then, the 'ideal-propagation' assumption is no longer valid, since the plasma sheath is a kind of dielectric medium that influences the propagation of electromagnetic waves, such as the nonuniform and time-varying distribution of the reflection coefficient in magnitude attenuation and in dispersion along the frequency axis [12]. The common propagation For CF-SAR with different modes, including stripmap, spotlight, sliding spotlight, and TOPS modes, etc., the main differences corresponding to the signal pattern between them depend on the beam steering and the position of the rotation center [10].…”

Section: Unified Modelmentioning

confidence: 99%

Curvilinear Flight Synthetic Aperture Radar (CF-SAR): Principles, Methods, Applications, Challenges and Trends

et al. 2022

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The research into curvilinear flight synthetic aperture radar (CF-SAR) is the inevitable result of the comprehensive practicality of SAR. The flight path of the SAR platform in real applications, which is highly nonlinear or curvy due to three-dimensional velocity and acceleration, cannot be described by the traditional uniform linear motion model. New mathematical models, signal characteristics, imaging algorithms, and system design criteria must be proposed and investigated for CF-SAR. This paper provides a comprehensive overview of CF-SAR. Firstly, the basic concept, unified model, and general signal characteristics of CF-SAR are defined, derived, and analyzed, respectively. Additionally, the advantages and drawbacks of current methodologies are reviewed. Discussions on the CF-SAR’s applications are presented from the perspective of typical platforms, new configurations, and advanced technologies, which are suitable means to fulfill the increasing user requirements. Finally, the challenges faced by CF-SAR are summarized, and some future trends for the study of CF-SAR are explored. Hopefully, this paper will serve as a reference for SAR researchers/engineers and stimulate the future development and actual application of CF-SAR.

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“…In addition, the frequency diversity method [6] and the Finite-Difference Time-Domain (FDTD) method [7,8] are also used to analyze the reflection characteristics of EM waves in the plasma sheath. Furthermore, some research also investigates the distortion mechanism of the echo's amplitude and phase considering the plasma sheath [9], and proposes corresponding compensation methods [10]. As to the polarization, few studies have explored the polarimetric impact of the plasma sheath on radar.…”

Section: Introductionmentioning

confidence: 99%

Polarization Characteristics Distortion for L-Band Fully Polarimetric Radar Subject to Magnetized Plasma Sheath

Guo,

Hu,

Shen

et al. 2024

Remote Sensing

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High-velocity reentry objects suffer from plasma sheath during reentry through the atmosphere, which affects the propagation characteristics of radar signals. The existing research mainly focuses on the time-frequency characteristics of radar signals, neglecting the polarization within the geomagnetic environment. In this article, the distortion of polarization characteristics for L-band fully polarimetric radar is analyzed, and the influence of the geomagnetic field is evaluated. Based on the Appleton–Hartree formula, the refractive index of the plasma sheath considering the geomagnetic field is derived and analyzed. The error model for the polarization deflection (PD) of radar waves is then established based on the phase screen model. The magnetized plasma sheath causes the deflection of the polarization plane for the radar signal, leading to distortion in the polarization characteristics and the attenuation of the echo amplitude. Considering the typical parameters of the plasma sheath, the influences of the electron density, collision frequency, the geomagnetic field and the radar frequency are analyzed quantitatively. Specifically, the PD anomaly phenomenon is analyzed and the corresponding analytical result of radar frequency is also derived. The relationship between the geomagnetic field and the PD, as well as the attenuation, is considered to be approximately linear. The absorption attenuation is primarily influenced by collision frequency and is immune to the geomagnetic field. In addition, the increasing electron density expands them, whereas the radar frequency and the collision frequency have the opposite effect. Simulations with real SAR data from ALOS-2 demonstrate the distortions resulting from the magnetized plasma sheath on the radar echoes in an L-band fully polarimetric radar system.

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Effect of Time-Varying Plasma Sheath on Hypersonic Vehicle-Borne Radar Target Detection

Cited by 14 publications

References 41 publications

Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence

Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence

Curvilinear Flight Synthetic Aperture Radar (CF-SAR): Principles, Methods, Applications, Challenges and Trends

Polarization Characteristics Distortion for L-Band Fully Polarimetric Radar Subject to Magnetized Plasma Sheath

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