Underwater Source Localization Using TDOA and FDOA Measurements With Unknown Propagation Speed and Sensor Parameter Errors

Zhang, Bingbing; Hu, Yongchang; Wang, Hongyi; Zhuang, Zhihao

doi:10.1109/access.2018.2852636

Cited by 41 publications

(27 citation statements)

References 38 publications

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“…Underwater positioning methods based on the frequency difference of arrival (FDOA) and TDOA have been proposed in [16] to address the acoustic-based localization in underwater environments. It considers the features of the acoustic signal propagation under undetermined acoustic speed propagation in the water.…”

Section: Related Workmentioning

confidence: 99%

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Sun

Esmaiel

2020

Sensors

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Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater environment. This paper proposes a TDOA-based localization algorithm for an underwater acoustic sensor network using the maximum-likelihood (ML) ratio criterion. To relax the complexity of the proposed localization complexity, we construct an auxiliary function, and use the majorization-minimization (MM) algorithm to solve it. The proposed localization algorithm proposed in this paper is called a T-MM algorithm. T-MM is applying the MM algorithm to the TDOA acoustic-localization technique. As the MM algorithm iterations are sensitive to the initial points, a gradient-based initial point algorithm is used to set the initial points of the T-MM scheme. The proposed T-MM localization scheme is evaluated based on squared position error bound (SPEB), and through calculation, we get the SPEB expression by the equivalent Fisher information matrix (EFIM). The simulation results show how the proposed T-MM algorithm has better performance and outperforms the state-of-the-art localization algorithms in terms of accuracy and computation complexity even under a high presence of underwater noise.

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Section: Related Workmentioning

confidence: 99%

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Sun

Esmaiel

2020

Sensors

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“…Furthermore, [27]- [29] investigated the TDoA and ToA localization algorithms. A closed form solution is considered by constructing a connection between the unknown source location and hybrid estimations and to discover the most suitable sensor nodes association, respectively.…”

Section: Related Workmentioning

confidence: 99%

Efficient and Accurate Target Localization in Underwater Environment

Ullah

Liu

et al. 2019

IEEE Access

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Localization is the basic feature of wireless sensor networks (WSN) and estimating the time difference of arrival (TDoA), and angle of arrival (AoA) is the most used schemes for localization. Underwater wireless sensor network (UWSN) is extensively used for data collection in an underwater environment for both military and civilian applications. Nevertheless, efficient and accurate localization algorithms are essential for the UWSN because of the dynamical property of the underwater surrounding. Also, data management, collection, and processing for WSNs have become a more active topic nowadays in computer science, such as database system and data mining. The objective of deploying the WSNs applications is to collect the real-time data, which has very challenging due to the capacity of communication and high data generated by WSNs. For this purpose, the time and location are the basic aspects when a sensor collects data, especially for the case of location-aware data. Many researchers have studied the underwater sensor nodes localization and they have considered the sensor location where the data is collected and most of them focused on the fixed sensor nodes. In this research work, energy efficient and accurate localization schemes are presented named as distance-based and angle-based schemes for the underwater environment with relatively less energy consumption and mean estimation errors (MEEs). The proposed schemes mainly focus on the localization of underwater nodes and especially on the MEEs in localization. The extensive simulation is performed to compare the proposed schemes with other counterpart schemes. The results show that the proposed schemes outperform other counterpart schemes in terms of MEEs of localization and energy consumption. INDEX TERMS Underwater wireless sensor networks (UWSNs), underwater localization, underwater acoustic sensor networks (UASNs). I. INTRODUCTION Water has covered 70 % of the land surface; basically, water is an infrequent substance that characterizes just 0.05 % of the land entire mass. However, water always plays an important role in the appearance of life on earth and especially for the living organism. The earth would be considered as a dead planet without the existence of water. The Underwater environment is still not well explored and researched for the betterment of human being life. Nowadays underwater communication technology has become an important part of our daily life and attracted more attention due to its broad applications in underwater. Those applications The associate editor coordinating the review of this manuscript and approving it for publication was Chun-Wei Tsai. include control systems, environmental monitoring, navigation and many more. Underwater Acoustic Sensor Networks (UASN) knowledge delivers novel openings to discover the underwater environment and as a consequence it expands our understanding of the environmental problems that we face in daily life, such as climate changes, animals life in oceans and the continuous changes in the inhabitants...

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“…Furthermore, underwater sensors may produce small fluctuations with the flow of seawater. To solve this problem, a TDOA/FDOA joint localisation algorithm is proposed in [16] for uncertain sensor parameter positions. There, the measurement equation is constructed by introducing sensor position noise.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike other literature, we develop the resulting problem to find a closed‐form solution through vector–matrix notations, hence avoiding the iterative or approximate methods. In this regard, the penalty function method is applied to formulate the constrained problem into an unconstrained problem. In [8, 15, 16], localisation algorithms all made the same approximation by ignoring the noise square term of the TDOA equation, which results in a lack of accuracy. This effect is more evident when the noise variance is greater than one.…”

Section: Introductionmentioning

confidence: 99%

Underwater TDOA/FDOA joint localisation method based on cross‐ambiguity function

Jiang

Zhang

Najafabadi³

et al. 2020

IET Radar, Sonar & Navigation

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Underwater Source Localization Using TDOA and FDOA Measurements With Unknown Propagation Speed and Sensor Parameter Errors

Cited by 41 publications

References 38 publications

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Efficient and Accurate Target Localization in Underwater Environment

Underwater TDOA/FDOA joint localisation method based on cross‐ambiguity function

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