A Time Difference of Arrival Based Target Motion Analysis for Localization of Underwater Vehicles

Alexandri, Talmon; Walter, Michael; Diamant, Roee

doi:10.1109/tvt.2021.3120201

Cited by 29 publications

(15 citation statements)

References 45 publications

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“…The localization of underwater nodes typically requires at least four reference nodes; however, due to the limited communication coverage of reference nodes, there may not be enough reference nodes for some nodes to be located. Many works have been proposed to tackle this problem and solve the problem of large-scale network localization [17,18,[34][35][36][37][38][39][40][41][42].…”

Section: Related Workmentioning

confidence: 99%

Fast Ray-Tracing-Based Precise Localization for Internet of Underwater Things without Prior Acknowledgment of Target Depth

Huang,

Meng,

Sun

et al. 2024

JMSE

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Underwater localization is one of the key techniques for positioning, navigation, timing (PNT) services that could be widely applied in disaster warning, underwater rescues and resource exploration. One of the reasons why it is difficult to achieve accurate positioning for underwater targets is due to the influence of uneven distribution of underwater sound velocity. The current sound-line correction positioning method mainly aims at scenarios with known target depth. However, for nodes that are non-cooperative nodes or lack depth information, sound-line tracking strategies cannot work well due to non-unique positional solutions. To solve this problem, we propose an iterative ray tracing 3D underwater localization (IRTUL) method for stratification compensation. To demonstrate the feasibility of fast stratification compensation, we first derive the signal path as a function of initial |grazing angle, and then prove that the signal propagation time and horizontal propagation distance are monotonic functions of the initial grazing angle, which guarantees the fast achievement of ray tracing. Simulation results indicate that IRTUL has the most significant correction effect in the depth direction, and the average accuracy has been improved by about 3 m compared to a localization model with constant sound velocity.

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Section: Related Workmentioning

confidence: 99%

Fast Ray-Tracing-Based Precise Localization for Internet of Underwater Things without Prior Acknowledgment of Target Depth

Huang,

Meng,

Sun

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Consequently, UASN is challenged by difficulties introduced by complicated underwater environments, such as limited bandwidth, limited energy, long propagation latency, low link quality, and more [22]. As such, a variety of UASN localisation algorithms are designed to accustom the underwater environments [23,24]. In ref.…”

Section: Introductionmentioning

confidence: 99%

A zeroing neurodynamics‐based location method for nodes in underwater acoustic sensor network

Wang

Deng

2023

CAAI Trans on Intel Tech

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Zeroing neurodynamics methodology, which dedicates to finding equilibrium points of equations, has been proven to be a powerful tool in the online solving of problems with considerable complexity. In this paper, a method for underwater acoustic sensor network (UASN) localisation is proposed based on zeroing neurodynamics methodology to preferably locate moving underwater nodes. A zeroing neurodynamics model specifically designed for UASN localisation is constructed with rigorous theoretical analyses of its effectiveness. The proposed zeroing neurodynamics model is compatible with some localisation algorithms, which can be utilised to eliminate error in non‐ideal situations, thus further improving its effectiveness. Finally, the effectiveness and compatibility of the proposed zeroing neurodynamics model are substantiated by examples and computer simulations.

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“…In general, the localization methods contain two main categories of two-step localization method and one-step localization method. Algorithms of two-step localization method [11][12][13][14][15][16][17][18][19][20] are commonly composed of two steps: first, each receiver station measures and computes the parameters related to the position of the target. The parameters involved in position include time difference of arrival (TDOA) [11][12][13], angle of arrival (AOA) [14][15][16][17], frequency difference of arrival (FDOA) [18][19][20], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms of two-step localization method [11][12][13][14][15][16][17][18][19][20] are commonly composed of two steps: first, each receiver station measures and computes the parameters related to the position of the target. The parameters involved in position include time difference of arrival (TDOA) [11][12][13], angle of arrival (AOA) [14][15][16][17], frequency difference of arrival (FDOA) [18][19][20], etc. The second step is to construct equations using the parameters to estimate the position of the target.…”

Section: Introductionmentioning

confidence: 99%

A Fast Direct Position Determination Algorithm for LFM Signal Based on Spectrum Detection

2022

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For the purpose of simultaneously estimating and locating the linear frequency modulation (LFM) emitter with unknown parameters, an innovative approach, i.e., Fast Direct Position Determination (FDPD), is introduced. The proposed algorithm is based on maximum likelihood estimation (MLE) and spectrum detection. To improve the accuracy and overcome the dramatic complexity of plain maximum likelihood formulation, we further derive the objective function equation of Direct Position Determination (DPD) algorithm and present an enhanced strategy to solve the highly nonlinear optimization problem. By combining the two-step localization method, one-step localization method, and short-time Fourier transform (STFT), our approach realizes jointly estimation of the transmitted signal parameters and emitter localization. Simulation results show that the proposed method is superior compared to the existing DPD, and two-step localization algorithms in terms of localization error and computational complexity, especially for low signal-to-noise ratio (SNR).

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A Time Difference of Arrival Based Target Motion Analysis for Localization of Underwater Vehicles

Cited by 29 publications

References 45 publications

Fast Ray-Tracing-Based Precise Localization for Internet of Underwater Things without Prior Acknowledgment of Target Depth

Fast Ray-Tracing-Based Precise Localization for Internet of Underwater Things without Prior Acknowledgment of Target Depth

A zeroing neurodynamics‐based location method for nodes in underwater acoustic sensor network

A Fast Direct Position Determination Algorithm for LFM Signal Based on Spectrum Detection

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