Parameter estimation of underwater impulsive noise with the Class B model

Zhang, Xuebo; Ying, Wenwei; Yang, Peixuan; Sun, Miao

doi:10.1049/iet-rsn.2019.0477

Cited by 59 publications

(47 citation statements)

References 18 publications

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“…Underwater acoustic channel noise is an important topic in the application practice of UACNs, as hydrostatic pressure effects (tides, waves, etc., caused by wind, rain, and seismic disturbances) and industrial behavior (e.g., surface sailing) remain one of the main reasons hindering the development of underwater acoustic communication [20][21][22]. Calculating the noise power σ 2 is a very complex challenge, because of the significant time-space-frequency variability of underwater acoustic channel noise [23,24]. Fortunately, σ 2 can be calculated from the corresponding power spectral density [15,25], which can be described as follows:…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

Yang

Wang

Fan

et al. 2022

Wireless Communications and Mobile Computing

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To solve the problems of poor quality of service and low energy efficiency of nodes in underwater multinode communication networks, a distributed power allocation algorithm based on reinforcement learning is proposed. The transmitter with reinforcement learning capability can select the power level autonomously to achieve the goal of getting higher user experience quality with lower power consumption. Firstly, we propose a distributed power optimization model based on the Markov decision process. Secondly, we further give a reward function suitable for multiobjective optimization. Finally, we present a distributed power allocation algorithm based on Q-learning and use it as an adaptive mechanism to enable each transmitter in the network to adjust the transmit power according to its own environment. The simulation results show that the proposed algorithm not only increases the total channel capacity of the system but also improves the energy efficiency of each transmitter.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

Yang

Wang

Fan

et al. 2022

Wireless Communications and Mobile Computing

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“…The PatternRecognition class is used to construct the clustering model based on the trajectory selected by a user and display the trajectory clustering results in graphical form. The class consists of the objects dbConfig, trajs, and clusterModel [ 27 ]. The methods mainly include TrajPartition() for trajectory partitioning, Clustering() for trajectory clustering modeling, Predict() for cluster prediction, and DisplayResult() for displaying the trajectory clustering result.…”

Section: Design Of Functional Modulesmentioning

confidence: 99%

The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS

Feng

Bing

Luo

et al. 2021

Sensors

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To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand.

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“…Today, high-precision gravity field maps, like remote sensing satellite images, SAR images and other satellite images, play an important role in the field of national economy and people's livelihood [4][5][6][7]. In the underwater navigation of submarines, the traditional sonar technology is unable to meet the requirements for high precision navigation at the seabed since it cannot receive any signal in deep waters, but a seabed high precision gravity map can be used to assist submarines in rapidly locating and avoiding the obstacles at seabed [1,8,9]. Hence, underwater gravity navigation entails high precision gravity measurement.…”

Section: Introductionmentioning

confidence: 99%

Effects and Prospects of the Vibration Isolation Methods for an Atomic Interference Gravimeter

Gong

Liu

Huang

et al. 2022

Sensors

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An atomic interference gravimeter (AIG) is of great value in underwater aided navigation, but one of the constraints on its accuracy is vibration noise. For this reason, technology must be developed for its vibration isolation. Up to now, three methods have mainly been employed to suppress the vibration noise of an AIG, including passive vibration isolation, active vibration isolation and vibration compensation. This paper presents a study on how vibration noise affects the measurement of an AIG, a review of the research findings regarding the reduction of its vibration, and the prospective development of vibration isolation technology for an AIG. Along with the development of small and movable AIGs, vibration isolation technology will be better adapted to the challenging environment and be strongly resistant to disturbance in the future.

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Parameter estimation of underwater impulsive noise with the Class B model

Cited by 59 publications

References 18 publications

Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS

Effects and Prospects of the Vibration Isolation Methods for an Atomic Interference Gravimeter

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