Radar Propagation Experiment in the North Sea: The Sylt Campaign

Danklmayer, Andreas; Förster, Jörg; Fabbro, Vincent; Biegel, Gregor; Brehm, Thorsten; Colditz, Paul; Castanet, Laurent; Hurtaud, Yvonick

doi:10.1109/tgrs.2017.2755506

Cited by 8 publications

(4 citation statements)

References 13 publications

(3 reference statements)

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“…On the one hand, maritime communication faces challenges such as long distances between BSs and users, significant transmission delays, and high interference [17]. It is difficult to obtain the CSI in the maritime environment [18], and the traditional communication resource allocation schemes based on CSI are difficult to employ in the maritime environment [19]. On the other hand, there is still a lack of relevant research on the distribution characteristics of communication users in maritime environments.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Resource Management in Maritime Communication Systems

Yao,

Hu,

et al. 2024

Sensors

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With the growing maritime economy, ensuring the quality of communication for maritime users has become imperative. The maritime communication system based on nearshore base stations enhances the communication rate of maritime users through dynamic resource allocation. A virtual queue-based deep reinforcement learning beam allocation scheme is proposed in this paper, aiming to maximize the communication rate. More particularly, to reduce the complexity of resource management, we employ a grid-based method to discretize the maritime environment. For the combinatorial optimization problem of grid and beam allocation under unknown channel state information, we model it as a sequential decision process of resource allocation. The nearshore base station is modeled as a learning agent, continuously interacting with the environment to optimize beam allocation schemes using deep reinforcement learning techniques. Furthermore, we guarantee that grids with poor channel state information can be serviced through the virtual queue method. Finally, the simulation results provided show that our proposed beam allocation scheme is beneficial in terms of increasing the communication rate.

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Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Resource Management in Maritime Communication Systems

Yao,

Hu,

et al. 2024

Sensors

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“…It reported a σ o value for the maximum observed peak of -24.5 dB. In [6], a very comprehensive trial (North Sea Sylt campaign) was reported which encompassed meteorological and oceanographic sensor data along with radar measurements at X, Ka and W bands. In terms of radar sea clutter, it presented range-time intensity plots from X and Ka-band data, but no amplitude or Doppler characteristics analysis at W-band were reported.…”

Section: Introductionmentioning

confidence: 99%

Amplitude Characteristics of Littoral Sea Clutter Data at K-band and W-band

Rahman

Vattulainen

Robertson

2022

2022 IEEE Radar Conference (RadarConf22)

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Sea clutter data at millimeter wave frequencies are quite limited in the literature. Recent advancements in millimeter wave radar technology have created a potential for its use in maritime surveillance and autonomy. Hence, collecting data at this frequency range is of great interest to both academia and industry. This study reports on a field trial conducted at St Andrews in winter 2020 to collect littoral sea clutter data using K-band (24 GHz) and W-band (94 GHz) radar systems. Extensive data collection was done during the trial, where this work specifically concentrates on analysis of the amplitude characteristics of the sea clutter returns. Analysis of the dataset shows that the radar backscatter was heavily dominated by seaspikes. The modal normalized radar cross section (NRCS) values for Bragg, burst and whitecap scattering are measured to be -47, -30 and -17 dB respectively at 24 GHz in horizontal polarization and -48, -26 and -12 dB respectively at 94 GHz in circular polarization, measured at grazing angles of 1-3°. The backscatter from the smooth surface is found to be below the noise floor equivalent NRCS (-65 dB). Also, the power spectrum analysis of range-time intensity plots is discussed, revealing information on the sea surface dynamics.

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“…An atmospheric duct is a unique phenomenon in the lower atmosphere, and electromagnetic waves can experience less attenuation in the trapped layer by limiting the spread of the wavefront from spherical to cylindrical expansion, where the waves are bent by atmospheric refraction [1,2]. A long-range transmission in microwave bands can also be realized.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

2022

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The evaporation duct height (EDH) can reflect the main characteristics of the near-surface meteorological environment, which is essential for designing a communication system under this propagation mechanism. This study proposes an EDH prediction network with multi-layer perception (MLP). Further, we construct a multi-dimensional EDH prediction model (multilayer-MLP-EDH) for the first time by adding spatial and temporal "extra data" derived from the meteorological measurements. The experimental results show that: 1) compared with the naval-postgraduate-school (NPS) model, the root-mean-square error (RMSE) of the meteorological-MLP-EDH model is reduced to 2.15 m, and the percentage improvement reached 54.00 %; 2) spatial and temporal parameters can reduce the RMSE to 1.54 m with an improvement of 66.96 %; 3) the multilayer-MLP- EDH model can match measurements well at both large and small scales by attaching meteorological parameters at extra height, the error is further reduced to 1.05 m, with 77.51 % improvement compared with the NPS model. The proposed model can significantly improve the prediction accuracy of the EDH and has great potential to improve the communication quality, reliability, and efficiency of ducting in evaporation ducts.

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Radar Propagation Experiment in the North Sea: The Sylt Campaign

Abstract: Abstract-This paper describes an experiment that was carried out in the North

Cited by 8 publications

References 13 publications

Deep Reinforcement Learning-Based Resource Management in Maritime Communication Systems

Deep Reinforcement Learning-Based Resource Management in Maritime Communication Systems

Amplitude Characteristics of Littoral Sea Clutter Data at K-band and W-band

A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

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