Enhanced Loran skywave delay estimation based on artificial neural network in low SNR environment

Zhang, Kai; Wan, Guobin; Xi, Xiaoli

doi:10.1049/iet-rsn.2019.0222

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…Recent studies on the detection of the skywave in the eLoran signal have attempted to improve the SNR of the eLoran signals at night [47], [48]. Another major factor affecting the eLoran SNR is atmospheric noise.…”

Section: Survey and Discussion For The Loran Signal Environment mentioning

confidence: 99%

eLoran: Resilient Positioning, Navigation, and Timing Infrastructure in Maritime Areas

et al. 2020

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Global navigation satellite system (GNSS) and localization technology have become highly compact and effective. Although GNSS has been proven to be vulnerable to jamming when the signal strength is weak, most vehicles rely on it for navigation. Compared to GNSS, enhanced Loran (eLoran) is more resistant to jamming and is recommended as a realistic alternative to GNSS. Therefore, South Korea embarked on an eLoran testbed development project to provide resilient navigation information for maritime users. The primary goal of this project was to demonstrate that the system can provide a 20-m position accuracy to the maritime user within a 30-km coverage of a differential Loran station. In this paper, we describe the development of the eLoran testbed implementation and the performance of its preliminary experimental results. To confirm the feasibility of the eLoran testbed performance, we installed an eLoran transmitter and two differential Loran stations and subsequently conducted a zero-baseline performance test at the Pyeongtaek differential Loran station with a new eLoran signal. We also conducted a survey to collect conventional Loran signals along the western and southern coastlines of South Korea. Our experimental results show that the eLoran system met the performance requirements of this project, as well as that the signal to noise ratio of the eLoran signals changed over the topographic characteristics of the propagation path. INDEX TERMS GNSS backup, eLoran testbed, resilient PNT, maritime navigation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: Survey and Discussion For The Loran Signal Environment mentioning

confidence: 99%

eLoran: Resilient Positioning, Navigation, and Timing Infrastructure in Maritime Areas

et al. 2020

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“…Since a Loran-C signal has a period of 10 μs, a 1 μs advance is equivalent to a–π/5 radian shift, a 1 μs delay is equivalent to a π/5 radian shift, and no time shift is equivalent to a zero radian shift. Typically, the Loran-C receiver receives a mixed signal containing SWI, groundwave signal, and noise [ 19 , 20 , 21 ]. The skywave-to-groundwave amplitude ratio is represented by λ , and the delay of the SWI is represented by τ .…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the demodulation performance determines the timing capability of the Loran-C receiver. In recent years, studies on Loran-C signal receiving methods have mainly focused on ways of enhancing the accuracy of TOA measurements, such as by signal acquisition and detection [ 16 , 17 , 18 ], skywave identification [ 19 , 20 , 21 , 22 ], cycle identification [ 23 , 24 , 25 ], and additional secondary phase factor correction [ 26 , 27 , 28 ]. However, little attention has been paid to enhancing the demodulation performance of the PPM.…”

Section: Introductionmentioning

confidence: 99%

Demodulation Method for Loran-C at Low SNR Based on Envelope Correlation–Phase Detection

Yuan

Yan

et al. 2020

Sensors

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Loran-C is the most important backup and supplement system for the global navigation satellite system (GNSS). However, existing Loran-C demodulation methods are easily affected by noise and skywave interference (SWI). Therefore, this article proposes a demodulation method based on Loran-C pulse envelope correlation–phase detection (EC–PD), in which EC has two implementation schemes, namely moving average-cross correlation and matched correlation, to reduce the effects of noise and SWI. The mathematical models of the EC, calculation of the signal-to-noise ratio (SNR) gain, and selection of the EC schemes are given. The simulation results show that compared with an existing method, the proposed method has clear advantages: (1) The demodulation SNR threshold under Gaussian channel is only −2 dB, a reduction of 12.5 dB; (2) The probability of the demodulated SNR threshold, being less than zero under the SWI environment, can reach 0.78, a 26-fold increase. The test results show that the average data availability of the proposed method is 3.3 times higher than that of the existing method. Thus, our demodulation method has higher engineering application value. This will improve the performance of the modern Loran-C system, making it a more reliable backup for the GNSS.

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“…As an emergency backup of the Global Navigation Satellite System (GNSS), the eLoran navigation system has potential application values in the fields of navigation, position and timing (PNT) [1][2][3][4]. Many countries (such as the South Korea [2], China [5]) have upgraded to eLoran system and are using it. In this system, the receiver is the key equipment used by the user segment, and its performance in complex environments is critical [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Normally, the signal simulator is required to model the signals within the channel to accurately characterise eLoran pulsed signal groups. The signal model accuracy mainly depends on the time of arrivals (TOAs) of ground-wave and sky-wave [5,[11][12][13][14]. The TOA of the ground-wave is affected by weather factors and has different degrees of additional time delay [2,13,15,16].…”

Section: Introductionmentioning

confidence: 99%

High‐precision simulation technology for enhanced Loran signal simulators based on Lagrange farrow structure filter

Yuan

et al. 2022

IET Radar Sonar & Navi

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In this study, a real‐time high accuracy signal simulation technique for an enhanced Loran (eLoran) signal simulator is proposed, which can be used to verify the performance of the eLoran receiver. The proposed method is based on a maximumly flat design of the low‐pass filter, and high simulation accuracy can be obtained in the low‐frequency band. The simulator structure is based on the variable fractional delay Farrow structure, which can regenerate eLoran signals with high‐precision time of arrival (TOA) in real‐time based on discrete samples on a hardware platform. By comparison of existing methods and the true TOA data validation, the proposed method has high performance for low‐frequency narrow‐band eLoran signals and the proposed technology can apply to arbitrary TOA real‐time simulations.

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Enhanced Loran skywave delay estimation based on artificial neural network in low SNR environment

Cited by 8 publications

References 9 publications

eLoran: Resilient Positioning, Navigation, and Timing Infrastructure in Maritime Areas

eLoran: Resilient Positioning, Navigation, and Timing Infrastructure in Maritime Areas

Demodulation Method for Loran-C at Low SNR Based on Envelope Correlation–Phase Detection

High‐precision simulation technology for enhanced Loran signal simulators based on Lagrange farrow structure filter

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