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Kumar S, Chinthaginjala R, C D, Kim TH, Abbas M, Pau G, Reddy NB. Enhancing underwater target localization through proximity-driven recurrent neural networks. Heliyon 2024; 10:e28725. [PMID: 38596026 PMCID: PMC11002063 DOI: 10.1016/j.heliyon.2024.e28725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/11/2024] Open
Abstract
Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise underwater target localization. This study goes into the field of underwater target localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, with a focus on mean estimation error as a performance metric. In complex and dynamic underwater environments, conventional localization systems frequently face challenges such as signal degradation, noise interference, and unstable hydrodynamic conditions. This paper presents a novel approach to employing RNNs to increase the accuracy of underwater target localization by exploiting the temporal dynamics of proximity-informed data. This method uses an RNN architecture to track changes in audio emissions from underwater targets sensed by a microphone network. Using the temporal correlations represented in the data, the RNN learns patterns indicative of target localization quickly and correctly. Furthermore, the addition of proximity-based features increases the model's ability to understand the relative distances between hydrophone nodes and the target, resulting in more accurate localization estimates. To evaluate the suggested methodology, thorough simulations and practical experiments were carried out in a variety of underwater environments. The results show that the RNN-based strategy beats conventional methods and works effectively even in difficult settings. The utility of the proximity-aware RNN model is demonstrated, in particular, by considerable reductions in the mean estimate error (MEE), an important performance measure.
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Affiliation(s)
- Sathish Kumar
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | | | - Dhanamjayulu C
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | - Tai-hoon Kim
- School of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, 50 Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea
| | - Mohammed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia
| | - Giovanni Pau
- Faculty of Engineering and Architecture, Kore University of Enna, 94100, Enna, Italy
| | - Nava Bharath Reddy
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | - Member, IEEE
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India
- School of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, 50 Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea
- Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia
- Faculty of Engineering and Architecture, Kore University of Enna, 94100, Enna, Italy
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Zhang P, Wen H, Xu Z, Zhao Z. Improving the Robustness of Time Difference of Arrival Estimation Based on the Energy Center of Gravity Rearrangement. SENSORS (BASEL, SWITZERLAND) 2023; 23:9720. [PMID: 38139566 PMCID: PMC10747976 DOI: 10.3390/s23249720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/20/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023]
Abstract
An accurate estimation of the time difference of arrival (TDOA) is crucial in localization, communication, and navigation. However, a low signal-to-noise ratio (SNR) can decrease the reliability of the TDOA estimation result. Therefore, this study aims to improve the performance of the TDOA estimation of dual-channel sensors for single-sound sources in low-SNR environments. This study introduces the theory of time rearrangement synchrosqueezing transform (TRST) into the time difference of arrival estimation. While the background noise TF points show random time delays, the signal time-frequency (TF) points originating from uniform directions that exhibit identical lags are considered in this study. In addition, the time difference rearrangement synchrosqueezing transform (TDST) algorithm is developed to separate the signal from the background noise by exploiting its distinct time delay characteristics. The implementation process of the proposed algorithm includes four main steps. First, a rough estimation of the time delay is performed by calculating the partial derivative of the short-time cross-power spectrum. Second, a rearrangement operation is conducted to separate the TF points of the signal and noise. Third, the TF points on both sides of the time-delay energy ridge are extracted. Finally, a refined TDOA estimation is realized by applying the inverse Fourier transformation on the extracted TF points. Furthermore, a second-order-based time difference reassigned synchrosqueezing transform algorithm is proposed to improve the robustness of the TDOA estimation by enhancing the TF energy aggregation. The proposed algorithms are verified by simulations and experiments. The results show that the proposed algorithms are more robust and accurate than the existing algorithms.
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Affiliation(s)
- Peng Zhang
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (P.Z.); (Z.Z.)
| | - Hongyuan Wen
- Taizhou Institute of Science and Technology, Nanjing University of Science and Technology, Taizhou 225300, China;
| | - Zhiyong Xu
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (P.Z.); (Z.Z.)
| | - Zhao Zhao
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (P.Z.); (Z.Z.)
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Sathish K, Chinthaginjala R, Kim W, Rajesh A, Corchado JM, Abbas M. Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy. SENSORS (BASEL, SWITZERLAND) 2023; 23:6973. [PMID: 37571756 PMCID: PMC10422378 DOI: 10.3390/s23156973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 08/13/2023]
Abstract
Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.
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Affiliation(s)
- Kaveripakam Sathish
- School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India; (K.S.); (R.C.)
| | - Ravikumar Chinthaginjala
- School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India; (K.S.); (R.C.)
| | - Wooseong Kim
- Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea;
| | - Anbazhagan Rajesh
- School of Electrical and Electronics Engineering, SASTRA University, Thanjavur 613401, India;
| | - Juan M. Corchado
- BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain;
- Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
- Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
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Yan S, Wu C, Deng H, Luo X, Ji Y, Xiao J. A Low-Cost and Efficient Indoor Fusion Localization Method. SENSORS (BASEL, SWITZERLAND) 2022; 22:5505. [PMID: 35898008 PMCID: PMC9371102 DOI: 10.3390/s22155505] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/08/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.
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Affiliation(s)
- Suqing Yan
- Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China; (S.Y.); (Y.J.)
- School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China;
| | - Chunping Wu
- School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China;
| | - Honggao Deng
- Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China; (S.Y.); (Y.J.)
| | - Xiaonan Luo
- Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China;
| | - Yuanfa Ji
- Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China; (S.Y.); (Y.J.)
- National & Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service, Guilin 541004, China
| | - Jianming Xiao
- Department of Science and Engineering, Guilin University, Guilin 541004, China;
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Wang H, Rubenstein M. Decentralized Localization in Homogeneous Swarms Considering Real-World Non-Idealities. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3095032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Liu Z, Zhang L, Wei H, Xiao Z, Qiu Z, Sun R, Pang F, Wang T. Underwater acoustic source localization based on phase-sensitive optical time domain reflectometry. OPTICS EXPRESS 2021; 29:12880-12892. [PMID: 33985034 DOI: 10.1364/oe.422255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 03/31/2021] [Indexed: 06/12/2023]
Abstract
This paper demonstrates an underwater localization system based on an improved phase-sensitive optical time domain reflectometry (φ-OTDR). To localize the underwater acoustic source, 3D-printed materials with relatively high Poisson's ratio and low elastic modulus are wrapped by single-mode optical fibers to serve as an L-shaped planar sensing array, yielding a high-fidelity retrieval of acoustic wave signals. Based on the time difference of arrival (TDOA) algorithm, the time delay of signals detected by multiple sensing elements is used to locate the underwater acoustic source. Consequently, the three-dimensional localization feasibility of the proposed system is experimentally verified, showing a measurement error of about 2% in the localization range. It indicates that the proposed scheme is of great potential for applications in the underwater environment, such as trajectory tracking, oil/gas pipeline security monitoring and coastal defense.
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