A Hybrid Localization Algorithm Based on Doppler Shift and AOA for an Underwater Mobile Node

Hao, Kun; Xue, Qixin; Li, Cheng; Yu, Kaicheng

doi:10.1109/access.2020.3028608

Cited by 21 publications

(12 citation statements)

References 25 publications

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“…Mobile devices often have low accuracy in underwater due to surrounding challenges, such as the rapid attenuation of radio waves. In [17], the localization algorithm has proposed considering Doppler shift and AOA parameters. The protocol uses the property of frequency shift in between the anchor and the mobile node.…”

Section: Related Workmentioning

confidence: 99%

3D Localization and Error Minimization in Underwater Sensor Networks

Kumar

Nguyen

Kandulna

et al. 2022

ACM Trans. Sen. Netw.

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Wireless sensor networks (WSNs) consist of nodes distributed in the region of interest (ROI) and forward the collected data to the sink. The node’s location plays a vital role in data forwarding to enhance network efficiency by reducing the packet drop rate and energy consumption. The WSNs scenarios such as tracking, smart city, and agriculture applications require location details to serve the objective. Assuming the 3D application space, a combination of received signal strength (RSS) and time of arrival (TOA) can be helpful for reliable range estimation of nodes. Notably, the anchor node can minimize localization error for non-line of sight (NLOS) signals. We proposed an error minimization protocol for localization of the sensor node, assuming that the anchor node’s location is known prior and can limit the receiving signal in LOS, single or twice reflection. We start to exploit the sensor node’s geometrical relationship and the anchor node for LOS and NLOS signals and address misclassification. We started initially from the erroneous node position, bound its volume in 3D space, and reduced volume with each iteration following the constraint. Our simulation result outperforms the traditional methods on many occasions, such as boundary volume and computational complexity.

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Section: Related Workmentioning

confidence: 99%

3D Localization and Error Minimization in Underwater Sensor Networks

Kumar

Nguyen

Kandulna

et al. 2022

ACM Trans. Sen. Netw.

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“…] T denotes the noise vector at the destination during the u-th relay transmission, n D,u (i) is modeled as complex AWGN with mean zero and variance σ 2 D /2 per dimension and G G G u is a N ×N channel matrix for the u-th relay to destination channel and is given in (11). It can be noted that C C C u is a circulant matrix as it is a product of two circulant matrices.…”

Section: Receiver Structurementioning

confidence: 99%

“…There are therefore many potential applications involving mobile platforms, such as the one involving underwater robotics [9], [10]. It also extends network coverage by providing LOS between the source and sensors, and between sensors and destination, which results in providing multiple communication links for higher data collection rates [11].…”

mentioning

confidence: 99%

Single Carrier Frequency Domain Detectors for Internet of Underwater Things

Aljanabi¹,

Alluhaibi²,

Ahmed³

et al. 2022

Preprint

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This paper proposes low complexity detection for internet of underwater things (IoUT)s communication. The signal is transmitted from the source to the destination using several sensors. To simplify the computational operations at the transmitter and the sensory nodes, a single carrier frequency domain equalizer (SC-FDE) is proposed and amplify-and-forward (AF) protocols are employed. Fast Fourier transform (FFT) and use of cyclic prefix (CP) are also proposed to simplify these algorithms when compared to time-domain equalization. As precise channel data is difficult to capture in underwater communications, the adaptive implementation of FDE is proposed as a solution that can be employed when the channel experiences a fast doppler shift. The two adaptive detectors are based on the least mean-square (LMS) and recursive least square (RLS) principles. Numerical simulations show that the performance of the bit error rate (BER) performance of the proposed detectors is close to that of the ideal minimum mean square error (MMSE).

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“…In this work, we focus on the probabilistic PL positioning since censored PL has a simple interpretation as the probability of measuring only noise. Any measurement, e.g., Doppler and angle-of-arrival (AoA) [ 26 ], not just PL or RSS, is potentially unavailable due to noise and the likelihood that a censored measurement could in principle be included. Notably, at a relatively short distance, e.g., in case of neighbor discovery [ 27 ], censoring does not happen.…”

Section: Noise-limited Path Loss and Positioningmentioning

confidence: 99%

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Karttunen

Valkama

Talvitie

2021

Sensors

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Positioning is considered one of the key features in various novel industry verticals in future radio systems. Since path loss (PL) or received signal strength-based measurements are widely available in the majority of wireless standards, PL-based positioning has an important role among positioning technologies. Conventionally, PL-based positioning has two phases—fitting a PL model to training data and positioning based on the link distance estimates. However, in both phases, the maximum measurable PL is limited by measurement noise. Such immeasurable samples are called censored PL data and such noisy data are commonly neglected in both the model fitting and in the positioning phase. In the case of censored PL, the loss is known to be above a known threshold level and that information can be used in model fitting and in the positioning phase. In this paper, we examine and propose how to use censored PL data in PL model-based positioning. Additionally, we demonstrate with several simulations the potential of the proposed approach for considerable improvements in positioning accuracy (23–57%) and improved robustness against PL model fitting errors.

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A Hybrid Localization Algorithm Based on Doppler Shift and AOA for an Underwater Mobile Node

Cited by 21 publications

References 25 publications

3D Localization and Error Minimization in Underwater Sensor Networks

3D Localization and Error Minimization in Underwater Sensor Networks

Single Carrier Frequency Domain Detectors for Internet of Underwater Things

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

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