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Global Positioning System (GPS) plays a critical role in navigation by utilizing satellite signals, but its accuracy in urban environments is often compromised by signal obstructions. Previous research has categorized GPS reception conditions into line-of-sight (LOS), non-line-of-sight (NLOS), and LOS+NLOS scenarios to enhance accuracy. This paper introduces a novel approach using quantum support vector machines (QSVM) with a ZZ feature map and fidelity quantum kernel to classify urban GPS signal reception conditions, comparing its performance against classical SVM methods. While classical SVM has been previously explored for this purpose, our study is the first to apply QSVM to this classification task. We conducted experiments using datasets from two distinct urban locations to train and evaluate SVM and QSVM models. Our results demonstrate that QSVM achieves superior classification accuracy compared to classical SVM for urban GPS signal datasets. Additionally, we emphasize the importance of appropriately scaling raw data when utilizing QSVM., Comment: Submitted to IEEE QCE 2024

In this study, we propose a novel positioning and searching system for emergency location services, namely the hyper-enhanced local positioning system (HELPS), which is applicable to all mobile phone users, including legacy feature phone users. In the case of an emergency, rescuers are dispatched with portable signal measurement equipment around the estimated location of the emergency caller. Each signal measurement device measures the uplink signal from the mobile phone of the caller. After calculating the rough location of the caller's mobile phone based on these measurements, rescuers can efficiently search for the caller using the received uplink signal strength. Thus, the positioning accuracy in a conventional sense is not a limitation for rescuers in finding the caller. HELPS is not a traditional positioning system but rather a system with humans in the loop designed to reduce search time in emergencies. HELPS can provide emergency location information even in environments where the GPS or Wi-Fi is not functional. Furthermore, for HELPS operation, no hardware changes or software installations are required on the caller's mobile phone., Comment: Submitted to IEEE Wireless Communications

Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data efficiency, we show for the first time, that in-context learning is sufficient to tackle multilingual TOD. To handle the challenging dialogue state tracking (DST) subtask, we break it down to simpler steps that are more compatible with in-context learning where only a handful of few-shot examples are used. We test our approach on the multilingual TOD dataset X-RiSAWOZ, which has 12 domains in Chinese, English, French, Korean, Hindi, and code-mixed Hindi-English. Our turn-by-turn DST accuracy on the 6 languages range from 55.6% to 80.3%, seemingly worse than the SOTA results from fine-tuned models that achieve from 60.7% to 82.8%; our BLEU scores in the response generation (RG) subtask are also significantly lower than SOTA. However, after manual evaluation of the validation set, we find that by correcting gold label errors and improving dataset annotation schema, GPT-4 with our prompts can achieve (1) 89.6%-96.8% accuracy in DST, and (2) more than 99% correct response generation across different languages. This leads us to conclude that current automatic metrics heavily underestimate the effectiveness of in-context learning.

Quantum computing holds the potential to solve problems that are practically unsolvable by classical computers due to its ability to significantly reduce time complexity. We aim to harness this potential to enhance ray casting, a pivotal technique in computer graphics for simplifying the rendering of 3D objects. To perform ray casting in a quantum computer, we need to encode the defining parameters of primitives into qubits. However, during the current noisy intermediate-scale quantum (NISQ) era, challenges arise from the limited number of qubits and the impact of noise when executing multiple gates. Through logic optimization, we reduced the depth of quantum circuits as well as the number of gates and qubits. As a result, the event count of correct measurements from an IBM quantum computer significantly exceeded that of incorrect measurements., Comment: Submitted to ITC-CSCC 2024

This paper presents ExeGPT, a distributed system designed for constraint-aware LLM inference. ExeGPT finds and runs with an optimal execution schedule to maximize inference throughput while satisfying a given latency constraint. By leveraging the distribution of input and output sequences, it effectively allocates resources and determines optimal execution configurations, including batch sizes and partial tensor parallelism. We also introduce two scheduling strategies based on Round-Robin Allocation and Workload-Aware Allocation policies, suitable for different NLP workloads. We evaluate ExeGPT on six LLM instances of T5, OPT, and GPT-3 and five NLP tasks, each with four distinct latency constraints. Compared to FasterTransformer, ExeGPT achieves up to 15.2x improvements in throughput and 6x improvements in latency. Overall, ExeGPT achieves an average throughput gain of 2.9x across twenty evaluation scenarios. Moreover, when adapting to changing sequence distributions, the cost of adjusting the schedule in ExeGPT is reasonably modest. ExeGPT proves to be an effective solution for optimizing and executing LLM inference for diverse NLP workload and serving conditions., Comment: Accepted to ASPLOS 2024 (summer cycle)

Several sensing techniques have been proposed for silent speech recognition (SSR); however, many of these methods require invasive processes or sensor attachment to the skin using adhesive tape or glue, rendering them unsuitable for frequent use in daily life. By contrast, impulse radio ultra-wideband (IR-UWB) radar can operate without physical contact with users' articulators and related body parts, offering several advantages for SSR. These advantages include high range resolution, high penetrability, low power consumption, robustness to external light or sound interference, and the ability to be embedded in space-constrained handheld devices. This study demonstrated IR-UWB radar-based contactless SSR using four types of speech stimuli (vowels, consonants, words, and phrases). To achieve this, a novel speech feature extraction algorithm specifically designed for IR-UWB radar-based SSR is proposed. Each speech stimulus is recognized by applying a classification algorithm to the extracted speech features. Two different algorithms, multidimensional dynamic time warping (MD-DTW) and deep neural network-hidden Markov model (DNN-HMM), were compared for the classification task. Additionally, a favorable radar antenna position, either in front of the user's lips or below the user's chin, was determined to achieve higher recognition accuracy. Experimental results demonstrated the efficacy of the proposed speech feature extraction algorithm combined with DNN-HMM for classifying vowels, consonants, words, and phrases. Notably, this study represents the first demonstration of phoneme-level SSR using contactless radar., Comment: Submitted to IEEE Access

Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost, we apply manual editing to automatically translated data. We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language. X-RiSAWOZ has more than 18,000 human-verified dialogue utterances for each language, and unlike most multilingual prior work, is an end-to-end dataset for building fully-functioning agents. The many difficulties we encountered in creating X-RiSAWOZ led us to develop a toolset to accelerate the post-editing of a new language dataset after translation. This toolset improves machine translation with a hybrid entity alignment technique that combines neural with dictionary-based methods, along with many automated and semi-automated validation checks. We establish strong baselines for X-RiSAWOZ by training dialogue agents in the zero- and few-shot settings where limited gold data is available in the target language. Our results suggest that our translation and post-editing methodology and toolset can be used to create new high-quality multilingual dialogue agents cost-effectively. Our dataset, code, and toolkit are released open-source., Comment: Accepted by ACL 2023 Findings

In regions where global navigation satellite systems (GNSS) signals are unavailable, such as underground areas and tunnels, GNSS simulators can be deployed for transmitting simulated GNSS signals. Then, a GNSS receiver in the simulator coverage outputs the position based on the received GNSS signals (e.g., Global Positioning System (GPS) L1 signals in this study) transmitted by the corresponding simulator. This approach provides periodic position updates to GNSS users while deploying a small number of simulators without modifying the hardware and software of user receivers. However, the simulator clock should be synchronized to the GNSS satellite clock to generate almost identical signals to the live-sky GNSS signals, which is necessary for seamless indoor and outdoor positioning handover. The conventional clock synchronization method based on the wired connection between each simulator and an outdoor GNSS antenna causes practical difficulty and increases the cost of deploying the simulators. This study proposes a wireless clock synchronization method based on a private time server and time delay calibration. Additionally, we derived the constraints for determining the optimal simulator coverage and separation between adjacent simulators. The positioning performance of the proposed GPS simulator-based indoor positioning system was demonstrated in the underground testbed for a driving vehicle with a GPS receiver and a pedestrian with a smartphone. The average position errors were 3.7 m for the vehicle and 9.6 m for the pedestrian during the field tests with successful indoor and outdoor positioning handovers. Since those errors are within the coverage of each deployed simulator, it is confirmed that the proposed system with wireless clock synchronization can effectively provide periodic position updates to users where live-sky GNSS signals are unavailable., Comment: Submitted to IEEE Access

In urban areas, dense buildings frequently block and reflect global positioning system (GPS) signals, resulting in the reception of a few visible satellites with many multipath signals. This is a significant problem that results in unreliable positioning in urban areas. If a signal reception condition from a certain satellite can be detected, the positioning performance can be improved by excluding or de-weighting the multipath contaminated satellite signal. Thus, we developed a machine-learning-based method of classifying GPS signal reception conditions using a dual-polarized antenna. We employed a decision tree algorithm for classification using three features, one of which can be obtained only from a dual-polarized antenna. A machine-learning model was trained using GPS signals collected from various locations. When the features extracted from the GPS raw signal are input, the generated machine-learning model outputs one of the three signal reception conditions: non-line-of-sight (NLOS) only, line-of-sight (LOS) only, or LOS+NLOS. Multiple testing datasets were used to analyze the classification accuracy, which was then compared with an existing method using dual single-polarized antennas. Consequently, when the testing dataset was collected at different locations from the training dataset, a classification accuracy of 64.47% was obtained, which was slightly higher than the accuracy of the existing method using dual single-polarized antennas. Therefore, the dual-polarized antenna solution is more beneficial than the dual single-polarized antenna solution because it has a more compact form factor and its performance is similar to that of the other solution., Comment: Submitted to IEEE ION PLANS 2023

The maximum likelihood (ML) estimator can be applied to localize a target mobile device using the RSS and TOA. However, the ML estimator for the RSS-TOA-based target localization problem is nonconvex and nonlinear, having no analytical solution. Therefore, the ML estimator should be solved numerically, unless it is relaxed into a convex or linear form. This study investigates the target localization performance and computational complexity of numerical methods for solving an ML estimator. The three widely used numerical methods are: grid search, gradient descent, and particle swarm optimization. In the experimental evaluation, the grid search yielded the lowest target localization root-mean-squared error; however, the 95th percentile error of the grid search was larger than those of the other two algorithms. The average code computation time of the grid search was extremely large compared with those of the other two algorithms, and gradient descent exhibited the lowest computation time., Comment: Submitted to the 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

Neural networks have complex structures, and thus it is hard to understand their inner workings and ensure correctness. To understand and debug convolutional neural networks (CNNs) we propose techniques for testing the channels of CNNs. We design FtGAN, an extension to GAN, that can generate test data with varying the intensity (i.e., sum of the neurons) of a channel of a target CNN. We also proposed a channel selection algorithm to find representative channels for testing. To efficiently inspect the target CNN's inference computations, we define unexpectedness score, which estimates how similar the inference computation of the test data is to that of the training data. We evaluated FtGAN with five public datasets and showed that our techniques successfully identify defective channels in five different CNN models., Comment: 7pages, AAAI2023

This study investigates unmanned aerial vehicle (UAV) trajectory planning strategies for localizing a target mobile device in emergency situations. The global navigation satellite system (GNSS)-based accurate position information of a target mobile device in an emergency may not be always available to first responders. For example, 1) GNSS positioning accuracy may be degraded in harsh signal environments and 2) in countries where emergency positioning service is not mandatory, some mobile devices may not report their locations. Under the cases mentioned above, one way to find the target mobile device is to use UAVs. Dispatched UAVs may search the target directly on the emergency site by measuring the strength of the signal (e.g., LTE wireless communication signal) from the target mobile device. To accurately localize the target mobile device in the shortest time possible, UAVs should fly in the most efficient way possible. The two popular trajectory optimization strategies of UAVs are greedy and predictive approaches. However, the research on localization performances of the two approaches has been evaluated only under favorable settings (i.e., under good UAV geometries and small received signal strength (RSS) errors); more realistic scenarios still remain unexplored. In this study, we compare the localization performance of the greedy and predictive approaches under realistic RSS errors (i.e., up to 6 dB according to the ITU-R channel model)., Comment: Submitted to ION ITM 2023

Target localization is essential for emergency dispatching situations. Maximum likelihood estimation (MLE) methods are widely used to estimate the target position based on the received signal strength measurements. However, the performance of MLE solvers is significantly affected by the initialization (i.e., initial guess of the solution or solution search space). To address this, a previous study proposed the semidefinite programming (SDP)-based MLE initialization. However, the performance of the SDP-based initialization technique is largely affected by the shadowing variance and geometric diversity between the target and receivers. In this study, a radio frequency (RF) fingerprinting-based MLE initialization is proposed. Further, a maximum likelihood problem for target localization combining RF fingerprinting is formulated. In the three test environments of open space, urban, and indoor, the proposed RF fingerprinting-aided target localization method showed a performance improvement of up to 63.31% and an average of 39.13%, compared to the MLE algorithm initialized with SDP. Furthermore, unlike the SDP-MLE method, the proposed method was not significantly affected by the poor geometry between the target and receivers in our experiments., Comment: Submitted to the 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)

Multipath and non-line-of-sight (NLOS) signals are the major causes of poor accuracy of a global navigation satellite system (GNSS) in urban areas. Despite the wide usage of the GNSS in populated urban areas, it is difficult to suggest a generalized method because multipath errors are user-specific errors that cannot be eliminated by the DGNSS or a real-time kinematic technique. This paper introduces a real-time multipath estimation and mitigation technique, which considers compensation for the time offset between constellations. It also presents a mode-switching algorithm between the DGNSS and multipath mitigating mode and shows that this technique can be effectively utilized for automobiles in a deep urban environment without any help from sensors other than GNSS. The availability is improved from 64% to 100% and the error RMS is reduced from 11.1 m to 1.2 m on Teheran-ro, Seoul, Korea. Because this method does not require prior information or additional sensor implementation for high-positioning performance in deep urban areas, it is expected to gain wide usage in not only the automotive industry but also future intelligent transportation systems.

Predicting the safety of urban roads for navigation via global navigation satellite systems (GNSS) signals is considered. To ensure safe driving of automated vehicles, the vehicle must plan its trajectory to avoid navigating on unsafe roads (e.g., icy conditions, construction zones, narrow streets, etc.). Such information can be derived from the roads' physical properties, vehicle's capabilities, and weather conditions. From a GNSS-based navigation perspective, the reliability of GNSS signals in different locales, which is heavily dependent on the road layout within the surrounding environment, is crucial to ensure safe automated driving. An urban road environment surrounded by tall objects can significantly degrade the accuracy and availability of GNSS signals. This article proposes an approach to predict the reliability of GNSS-based navigation to ensure safe urban navigation. Satellite navigation reliability at a given location and time on a road is determined based on the probabilistic position error bound of the vehicle-mounted GNSS receiver. A metric for GNSS reliability for ground vehicles is suggested, and a method to predict the conservative probabilistic error bound of the GNSS navigation solution is proposed. A satellite navigation reliability map is generated for various navigation applications. As a case study, the reliability map is used in the proposed optimization problem formulation for automated ground vehicle safety-constrained path planning., Comment: Submitted to IEEE Intelligent Transportation Systems Magazine

Ground-based Augmentation System (GBAS) augments Global Navigation Satellite Systems (GNSS) to support the precision approach and landing of aircraft. To guarantee integrity, existing single-frequency GBAS utilizes position-domain geometry screening to eliminate potentially unsafe satellite geometries by inflating one or more broadcast GBAS parameters. However, GBAS availability can be drastically impacted in low-latitude regions where severe ionospheric conditions have been observed. Thus, we developed a novel geometry-screening algorithm in this study to improve GBAS availability in low-latitude regions. Simulations demonstrate that the proposed method can provide 5-8 percentage point availability enhancement of GBAS at Gale\~ao airport near Rio de Janeiro, Brazil, compared to existing methods., Comment: Submitted to IEEE Transactions on Intelligent Transportation Systems

The Stretched-FrOnt-Leg (SFOL) pulse is a high-accuracy distance measuring equipment (DME) pulse developed to support alternative positioning and navigation for aircraft during global navigation satellite system outages. To facilitate the use of the SFOL pulse, it is best to use legacy DMEs that are already deployed to transmit the SFOL pulse, rather than the current Gaussian pulse, through software changes only. When attempting to transmit the SFOL pulse in legacy DMEs, the greatest challenge is the pulse shape distortion caused by the pulse-shaping circuits and power amplifiers in the transmission unit such that the original SFOL pulse shape is no longer preserved. This letter proposes an inverse-learning-based DME digital predistortion method and presents successfully transmitted SFOL pulses from a testbed based on a commercial legacy DME that was designed to transmit Gaussian pulses., Comment: Submitted to IEEE Transactions on Aerospace and Electronic Systems

Neural network training requires a large amount of computation and thus GPUs are often used for the acceleration. While they improve the performance, GPUs are underutilized during the training.This paper proposes out-of-order (ooo) backprop, an effective scheduling technique for neural network training. By exploiting the dependencies of gradient computations, ooo backprop enables to reorder their executions to make the most of the GPU resources. We show that the GPU utilization in single-GPU, data-parallel, and pipeline-parallel training can be commonly improve by applying ooo back-prop and prioritizing critical operations. We propose three scheduling algorithms based on ooo backprop. For single-GPU training, we schedule with multi-stream out-of-order computation to mask the kernel launch overhead. In data-parallel training, we reorder the gradient computations to maximize the overlapping of computation and parameter communication; in pipeline-parallel training, we prioritize critical gradient computations to reduce the pipeline stalls.We evaluate our optimizations with twelve neural networks including a light-weight computer vision model (MobileNet) and largeNLP models (BERT and GPT-3) with up to forty eight V100 GPUs.Our scheduling algorithms effectively improve the performance of single-GPU training as well as data- and pipeline-parallel training.Compared to the respective state of the art training systems, the throughput is substantially improved for single-GPU, data-parallel, and pipeline-parallel training.

The vulnerabilities of global navigation satellite systems (GNSSs) to radio frequency jamming and spoofing have attracted significant research attention. In particular, the large-scale jamming incidents that occurred in South Korea substantiate the practical importance of implementing a complementary navigation system. This letter briefly summarizes the efforts of South Korea to deploy an enhanced long-range navigation (eLoran) system, which is a terrestrial low-frequency radio navigation system that can complement GNSSs. After four years of research and development, the Korean eLoran testbed system has been recently deployed and is operational since June 1, 2021. Although its initial performance at sea is satisfactory, navigation through a narrow waterway is still challenging because a complete survey of the additional secondary factor (ASF), which is the largest source of error for eLoran, is practically difficult in a narrow waterway. This letter proposes an alternative way to survey the ASF in a narrow waterway and improve the ASF map generation methods. Moreover, the performance of the proposed approach was validated experimentally., Comment: Submitted to IEEE Transactions on Aerospace and Electronic Systems

Accurate prediction of human movements is required to enhance the efficiency of physical human-robot interaction. Behavioral differences across various users are crucial factors that limit the prediction of human motion. Although recent neural network-based modeling methods have improved their prediction accuracy, most did not consider an effective adaptations to different users, thereby employing the same model parameters for all users. To deal with this insufficiently addressed challenge, we introduce a meta-learning framework to facilitate the rapid adaptation of the model to unseen users. In this study, we propose a model structure and a meta-learning algorithm specialized to enable fast user adaptation in predicting human movements in cooperative situations with robots. The proposed prediction model comprises shared and adaptive parameters, each addressing the user's general and individual movements. Using only a small amount of data from an individual user, the adaptive parameters are adjusted to enable user-specific prediction through a two-step process: initialization via a separate network and adaptation via a few gradient steps. Regarding the motion dataset that has 20 users collaborating with a robotic device, the proposed method outperforms existing meta-learning and non-meta-learning baselines in predicting the movements of unseen users., Comment: To be published in IEEE Robotics and Automation Letters (RA-L)

The Global Positioning System (GPS) has become the most widely used positioning, navigation, and timing system. However, the vulnerability of GPS to radio frequency interference has attracted significant attention. After experiencing several incidents of intentional high-power GPS jamming trials by North Korea, South Korea decided to deploy the enhanced long-range navigation (eLoran) system, which is a high-power terrestrial radio-navigation system that can complement GPS. As the first phase of the South Korean eLoran program, an eLoran testbed system was recently developed and declared operational on June 1, 2021. Once its operational performance is determined to be satisfactory, South Korea plans to move to the second phase of the program, which is a nationwide eLoran system. For the optimal deployment of additional eLoran transmitters in a nationwide system, it is necessary to properly simulate the expected positioning accuracy of the said future system. In this study, we propose enhanced eLoran accuracy simulation methods based on a land cover map and transmitter jitter estimation. Using actual measurements over the country, the simulation accuracy of the proposed methods was confirmed to be approximately 10%-91% better than that of the existing Loran (i.e., Loran-C and eLoran) positioning accuracy simulators depending on the test locations., Comment: Submitted to IEEE Access

The recently advanced robotics technology enables robots to assist users in their daily lives. Haptic guidance (HG) improves users' task performance through physical interaction between robots and users. It can be classified into optimal action-based HG (OAHG), which assists users with an optimal action, and user prediction-based HG (UPHG), which assists users with their next predicted action. This study aims to understand the difference between OAHG and UPHG and propose a combined HG (CombHG) that achieves optimal performance by complementing each HG type, which has important implications for HG design. We propose implementation methods for each HG type using deep learning-based approaches. A user study (n=20) in a haptic task environment indicated that UPHG induces better subjective evaluations, such as naturalness and comfort, than OAHG. In addition, the CombHG that we proposed further decreases the disagreement between the user intention and HG, without reducing the objective and subjective scores., Comment: Accepted to ACM 2021 CHI Conference on Human Factors in Computing Systems (CHI 2021)

The vulnerability of Global Positioning System (GPS) receivers to jammers is a major concern owing to the extremely weak received signal power of GPS. Researches have been conducted on a variety of antenna array techniques to be used as countermeasures to GPS jammers, and their antijamming performance is known to be greater than that of single antenna methods. However, the application of antenna arrays remains limited because of their size, cost, and computational complexity. This study proposes and experimentally validates a novel space-time-polarization domain adaptive processing for a single-element dual-polarized antenna (STPAPS) by focusing on the polarization diversity of a dual-polarized antenna. The mathematical models of arbitrarily polarized signals received by dual-polarized antenna are derived, and an appropriate constraint matrix for dual-polarized-antenna-based GPS antijam is suggested. To reduce the computational complexity of the constraint matrix approach, the eigenvector constraint design scheme is adopted. The performance of STPAPS is quantitively and qualitatively evaluated through experiments as follows. 1) The carrier-to-noise-density ratio (C/N0) of STPAPS under synthetic jamming is demonstrated to be higher than that of the previous minimum mean squared error (MMSE) or minimum variance distortionless response (MVDR) based dual-polarized antenna methods. 2) The strengths and weaknesses of STPAPS are qualitatively compared with those of the previous single-element dual-polarized antenna methods that are not based on the MMSE or MVDR algorithms. 3) The characteristics of STPAPS (in terms of the directions and polarizations of the GPS and jamming signals) are compared with those of the conventional two-element single-polarized antenna array method, which has the same degree of freedom as that of STPAPS., Comment: Submitted to IEEE Transactions on Aerospace and Electronic Systems

An integrity-based path planning strategy for autonomous ground vehicle (AGV) navigation in urban environments is developed. The vehicle is assumed to navigate by utilizing cellular long-term evolution (LTE) signals in addition to Global Positioning System (GPS) signals. Given a desired destination, an optimal path is calculated, which minimizes a cost function that considers both the horizontal protection level (HPL) and travel distance. The constraints are that (i) the ratio of nodes with faulty signals to the total nodes be lower than a maximum allowable ratio and (ii) the HPLs along each candidate path be lower than the horizontal alert limit (HAL). To predict the faults and HPL before the vehicle is driven, GPS and LTE pseudoranges along the candidate paths are generated utilizing a commercial ray-tracing software and three-dimensional (3D) terrain and building maps. Simulated pseudoranges inform the path planning algorithm about potential biases due to reflections from buildings in urban environments. Simulation results are presented showing that the optimal path produced by the proposed path planning strategy has the minimum average HPL among the candidate paths., Comment: Submitted to ION GNSS+ 2020

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 2020

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 2020

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 2020

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 2020

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 2020

Global Positioning System (GPS) and inertial measurement unit (IMU) sensors are commonly integrated using the extended Kalman filter (EKF), for achieving better navigation performance. However, because of nonlinearity, the performance of the EKF is affected by the initial state estimation errors, and the navigation solutions, including the attitude, diverge rapidly as the initial errors increase. This paper analyzes the data obtained from an outdoor experiment, and investigates the effect of the initial errors on the attitude estimation performance using EKF, which is used in loosely coupled low-cost smartphone GPS/IMU sensors., Comment: Submitted to ICCAS 2020

In this study, we applied reinforcement learning based on the proximal policy optimization algorithm to perform motion planning for an unmanned aerial vehicle (UAV) in an open space with static obstacles. The application of reinforcement learning through a real UAV has several limitations such as time and cost; thus, we used the Gazebo simulator to train a virtual quadrotor UAV in a virtual environment. As the reinforcement learning progressed, the mean reward and goal rate of the model were increased. Furthermore, the test of the trained model shows that the UAV reaches the goal with an 81% goal rate using the simple reward function suggested in this work., Comment: Submitted to ICCAS 2020

With the increasing demand for location-based services, localization technology research has recently intensified. Received signal strength (RSS)-based localization has the advantage of simplicity. However, as RSS-based localization requires the path-loss model parameters, it is difficult to use in place on which those parameters are unknown. In prior research, a transmitter localization algorithm with multiple stationary receivers was proposed for use under unknown path-loss exponent (PLE) conditions. However, if a mobile receiver is utilized, the localization would be possible with a single receiver alone. In this paper, we suggest a method of RSS-based LTE base station (BS) localization with a single mobile receiver when the PLE is unknown. We also propose an efficient mobile-receiver movement method to improve the PLE estimation and BS localization accuracy. Simulation results demonstrate the performance of the proposed methods., Comment: Submitted to ICTC 2020