A Survey on Underwater Localization, Localization Techniques and Its Algorithms

Ullah, Inam; Gao, Mingsheng; Kamal, Mian Muhammad; Khan, Zubair

doi:10.2991/eeeis-17.2017.35

Cited by 16 publications

(12 citation statements)

References 16 publications

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“…The network design principles for each network layer have been discussed in [9,10]. The authors explore potential applications and research directions for underwater sensor networking [11], such as monitoring, navigation and localization [12]. In the earlystage research, the design of UWA network is commonly performed in a layered structure, typically including the physical layer, the data link layer and the network layer [9][10][11].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks

Zhang

Shen

et al. 2022

Remote Sensing

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In the complex and dynamically varying underwater acoustic (UWA) channel, cooperative communication can improve throughput for UWA sensor networks. In this paper, we design a reasonable relay selection strategy for efficient cooperation with reinforcement learning (RL), considering the characteristics of UWA channel variation and long transmission delay. The proposed scheme establishes effective state and reward expression to better reveal the relationship between RL and UWA environment. Meanwhile, simulated annealing (SA) algorithm is integrated with RL to improve the performance of relay selection, where exploration rate of RL is dynamically adapted by SA optimization through the temperature decline rate. Furthermore, the fast reinforcement learning (FRL) strategy with pre-training process is proposed for practical UWA network implementation. The whole proposed SA-FRL scheme has been evaluated by both simulation and experimental data. The simulation and experimental results show that the proposed relay selection scheme can converge more quickly than classical RL and random selection with the increase of the number of iterations. The reward, access delay and data rate of SA-FRL can converge at the highest value and are close to the ideal optimum value. All in all, the proposed SA-FRL relay selection scheme can improve the communication efficiency through the selection of the relay nodes with high link quality and low access delay.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks

Zhang

Shen

et al. 2022

Remote Sensing

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“…No matter how many leader and follower AUVs are in the system, each follower AUV is independent, therefore all follower AUVs can obtain measurement information from the same single leader AUV at each sampling time step. The multi-AUV cooperative positioning system proposed in this paper can be applied in many aspects, such as improving ocean exploration, collecting oceanographic data, and ecological applications such as water quality and biological monitoring [13].…”

Section: Introductionmentioning

confidence: 99%

Consistent Extended Kalman Filter-Based Cooperative Localization of Multiple Autonomous Underwater Vehicles

Zhang

2022

Sensors

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In order to solve the problem of inconsistent state estimation when multiple autonomous underwater vehicles (AUVs) are co-located, this paper proposes a method of multi-AUV co-location based on the consistent extended Kalman filter (EKF). Firstly, the dynamic model of cooperative positioning system follower AUV under two leaders alternately transmitting navigation information is established. Secondly, the observability of the standard linearization estimator based on the lead-follower multi-AUV cooperative positioning system is analyzed by comparing the subspace of the observable matrix of state estimation with that of an ideal observable matrix, it can be concluded that the estimation of state by standard EKF is inconsistent. Finally, aiming at the problem of inconsistent state estimation, a consistent EKF multi-AUV cooperative localization algorithm is designed. The algorithm corrects the linearized measurement values in the Jacobian matrix for cooperative positioning, ensuring that the linearized estimator can obtain accurate measurement values. The positioning results of the follower AUV under dead reckoning, standard EKF, and consistent EKF algorithms are simulated, analyzed, and compared with the real trajectory of the following AUV. The simulation results show that the follower AUV with a consistent EKF algorithm can keep synchronization with the leader AUV more stably.

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“…In traditional underwater detection technology, acoustic-based underwater localization techniques have been widely studied by researchers [3][4][5]. They have achieved good localization results, but near-field targets are susceptible to mutual interference, such as noise between marks [6,7].…”

Section: Introductionmentioning

confidence: 99%

Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method

Hang

Wang

et al. 2022

Applied Sciences

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The lateral line system is an essential mechanosensory organ for organisms such as fish; it perceives the fluid environment in the near-field through the neuromasts on the lateral line system, supporting behaviors (e.g., obstacle avoidance and predation in fish). Inspired by the near-field perception ability of fish, we propose an artificial lateral line system composed of pressure sensors that respond to a target’s relative position by measuring the pressure change of the target vibration near the lateral line. Based on the shortcomings of the idealized constrained modeling approach, a multilayer perceptron network was built in this paper to process the pressure signal and predict the coordinates on a two-dimensional plane. Previous studies primarily focused on the localization of a single dipole source and rarely considered the localization of multiple vibration sources. In this paper, we explore the localization of numerous dipole sources of the same and different frequency vibrations based on the prediction of the two-dimensional coordinates of double dipoles. The experimental results show that the mutual interference of two vibration sources causes an increase in the localization error. Compared with multiple sources of vibration at the same frequency, the positioning accuracies of various vibration sources at different frequencies are higher. In addition, we explored the effects of the number of sensors on the localization results.

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A Survey on Underwater Localization, Localization Techniques and Its Algorithms

Cited by 16 publications

References 16 publications

Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks

Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks

Consistent Extended Kalman Filter-Based Cooperative Localization of Multiple Autonomous Underwater Vehicles

Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method

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