9 research outputs found
Effect of Outlier Removal from Temporal ASF Corrections on Multichain Loran Positioning Accuracy
The widely used global navigation satellite systems (GNSSs) are vulnerable to
radio frequency interference (RFI). Long-range navigation (Loran), a
terrestrial navigation system, can compensate for this weakness; however, it
suffers from low positioning accuracy, and studies are under way to improve its
positioning performance. One such study has proposed the multichain Loran
positioning method that uses the signals of transmitting stations belonging to
different chains. Although the multichain Loran positioning performance is
superior to the performance of conventional methods, the additional secondary
factor (ASF) can still degrade its positioning accuracy. To mitigate the
effects of temporal ASF, which is one of the ASF components, it is necessary to
obtain temporal correction data from a nearby reference station at a known
location. In this study, an experiment is performed to verify the effect of
removing the outliers in the temporal correction data on the multichain Loran
positioning accuracy.Comment: Submitted to ICCAS 202
Development of Record and Management Software for GPS/Loran Measurements
In this paper, a software implementation that records Global Positioning
System (GPS) and long-range navigation (Loran) measurement data output from an
integrated GPS/Loran receiver and organizes them based on time is proposed. The
purpose of the developed software is to collect measurements from multiple
Loran transmitter chains for performance analysis of navigation methods using
Loran, and to organize the data based on time to make it easy to use them. In
addition, GPS measurements are also collected and managed as ground truth data
for performance analysis. The implemented software consists of three modules:
recording, classification, and conversion. The recording module records raw
text data streamed from the receiver, and the classification module classifies
the recorded text data according to the message format. The conversion module
parses the classified text data, sorts GPS and Loran measurements based on
timestamp, and outputs them according to the software platform of the user to
analyze the measurements. Each module of the software runs automatically
without user intervention. The functionality of the implemented software was
verified using GPS and Loran measurements collected over 24 h from an actual
integrated GPS/Loran receiver.Comment: Submitted to ICCAS 202
Empirical Modeling of Variance in Medium Frequency R-Mode Time-of-Arrival Measurements
The R-Mode system, an advanced terrestrial integrated navigation system, is
designed to address the vulnerabilities of global navigation satellite systems
(GNSS) and explore the potential of a complementary navigation system. This
study aims to enhance the accuracy of performance simulation for the medium
frequency (MF) R-Mode system by modeling the variance of time-of-arrival (TOA)
measurements based on actual data. Drawing inspiration from the method used to
calculate the standard deviation of time-of-reception (TOR) measurements in
Loran, we adapted and applied this approach to the MF R-Mode system. Data were
collected from transmitters in Palmi and Chungju, South Korea, and the
parameters for modeling the variance of TOA were estimated.Comment: 4 pages, 2 figure
Ground Truth Generation Algorithm for Medium-Frequency R-Mode Skywave Detection
With the advancement of transportation vehicles, the importance and utility
of navigation systems providing positioning, navigation, and timing (PNT)
information have been increasing. Global navigation satellite systems (GNSS)
are widely used navigation systems, but they are vulnerable to radio frequency
interference (RFI), resulting in disruptions of satellite navigation signals.
Recognizing this limitation, extensive research is being conducted on
alternative navigation systems. In the maritime industry, ongoing research
focuses on a groundbased integrated navigation system called R-Mode. R-Mode
utilizes medium frequency (MF) differential GNSS (DGNSS) and very
high-frequency data exchange system (VDES) signals as ranging signals for
positioning and incorporates the existing ground-based navigation system known
as enhanced long-range navigation (eLoran). However, MF R-Mode, which uses MF
DGNSS signals for positioning, exhibits significant performance differences
between daytime and nighttime due to skywave interference caused by signals
reflecting off the ionosphere. In this study, we propose a skywave ground truth
generation algorithm that is crucial for studying mitigation methods for MF
R-Mode skywave interference. Furthermore, we demonstrate the proposed algorithm
using field-test data.Comment: Submitted to ICTC 202
Development of an R-Mode Simulator Using MF DGNSS Signals
With the development of positioning, navigation, and timing (PNT)
information-based industries, PNT information is becoming increasingly
important. Therefore, various navigation studies have been actively conducted
to back up global positioning system (GPS) in scenarios in which it is
disabled. Ranging using signals of opportunity (SoOP) has the advantage of
infrastructure already being in place. Among them, the ranging mode (R-Mode) is
a technology that uses available SoOPs such as a medium frequency (MF)
differential global navigation satellite System (DGNSS) signal that has
recently been recognized for its potential for navigation and is currently
under research. In this study, we developed a signal simulator that considers
the characteristics of MF DGNSS signals and skywaves used in R-Mode.Comment: Submitted to ICCAS 202
Practical Simplified Indoor Multiwall Path-Loss Model
Over the past few decades, attempts had been made to build a suitable channel
prediction model to optimize radio transmission systems. It is particularly
essential to predict the path loss due to the blockage of the signal, in indoor
radio system applications. This paper proposed a multiwall path-loss
propagation model for an indoor environment, operating at a transmission
frequency of 2.45 GHz in the industrial, scientific, and medical (ISM) radio
band. The effects of the number of the walls to be traversed along the radio
propagation path are considered in the model. To propose the model, the
previous works on well-known indoor path loss models are discussed. Then, the
path loss produced by the intervening walls in the propagation path is
measured, and the terms representing the loss factors in the theoretical
pathloss model are modified. The analyzed results of the path loss factors
acquired at 2.45 GHz are presented. The proposed path-loss model simplifies the
loss factor term with an admissible assumption of the indoor environment and
predicts the path-loss factor accurately.Comment: Submitted to ICCAS 202
A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment
Alternative navigation technology to global navigation satellite systems
(GNSSs) is required for unmanned ground vehicles (UGVs) in multipath
environments (such as urban areas). In urban areas, long-term evolution (LTE)
signals can be received ubiquitously at high power without any additional
infrastructure. We present a machine learning approach to estimate the range
between the LTE base station and UGV based on the LTE channel impulse response
(CIR). The CIR, which includes information of signal attenuation from the
channel, was extracted from the LTE physical layer using a software-defined
radio (SDR). We designed a convolutional neural network (CNN) that estimates
ranges with the CIR as input. The proposed method demonstrated better ranging
performance than a received signal strength indicator (RSSI)-based method
during our field test.Comment: Submitted to IEEE/IEIE ICCE-Asia 202
Preliminary Analysis of Skywave Effects on MF DGNSS R-Mode Signals During Daytime and Nighttime
Accurate positioning, navigation, and timing (PNT) performance are
prerequisites for several technologies today. In a marine environment, it is
difficult to visually identify one's position accurately, leading to safety
concerns. Currently, PNT information is provided mainly from Global Navigation
Satellite Systems (GNSS); however, it is vulnerable to radio frequency
interference, spoofing, and ionospheric anomaly. Therefore, research on a
backup system is needed. Ranging Mode (R-Mode), a terrestrial integrated
navigation system, is being investigated for use in a marine environment.
R-Mode is a positioning technology that integrates terrestrial signals of
opportunity such as medium frequency (MF) differential GNSS (DGNSS), very high
frequency (VHF) automatic identification system (AIS), and enhanced long-range
navigation (eLoran) signals. Previous studies in Europe show that signals in
the MF band differ greatly in accuracy between daytime and nighttime. This
difference is primarily caused by skywave. In this study, the MF DGNSS R-Mode
signal transmitted from Chungju, Korea was received in Daesan and Daejeon,
Korea. The skywave effect during daytime and nighttime was compared and
investigated. In addition, the continuous wave intensity of the R-Mode signal
was increased during the nighttime to compare its effect on the measurement
accuracy
Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments
A neural network (NN)-based approach for indoor localization via cellular
long-term evolution (LTE) signals is proposed. The approach estimates, from the
channel impulse response (CIR), the range between an LTE eNodeB and a receiver.
A software-defined radio (SDR) extracts the CIR, which is fed to a long
short-term memory model (LSTM) recurrent neural network (RNN) to estimate the
range. Experimental results are presented comparing the proposed approach
against a baseline RNN without LSTM. The results show a receiver navigating for
100 m in an indoor environment, while receiving signals from one LTE eNodeB.
The ranging root-mean squared error (RMSE) and ranging maximum error along the
receiver's trajectory were reduced from 13.11 m and 55.68 m, respectively, in
the baseline RNN to 9.02 m and 27.40 m, respectively, with the proposed
RNN-LSTM.Comment: Submitted to ICCAS 202