• Journal of Positioning, Navigation, and Timing
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• 제10권4호
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• pp.263-269
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• 2021

The carrier-based differential global navigation satellite system (CD-GNSS) has been garnering significant attention as a promising technology for unmanned vehicles for its high accuracy. The CD-GNSS systems to be used for safety-critical applications should provide a certain level of integrity. The integrity of these systems must be analyzed under various conditions, including fault-free and satellite fault conditions. The systems should be able to detect the faults that can cause large biases on the user position errors and quantify the integrity risk by computing the protection level (PL) to protect the user against the faults that are left undetected. Prior work has derived and investigated the PL for the fault-free condition. In this study, the integrity of the CD-GNSS system under the fault condition is analyzed. The position errors caused by the satellite's fault are compared with the fault-free PL (PL_H0) to verify whether the integrity requirement can be met without computing the PLs for the fault conditions. The simulations are conducted by assuming the ephemeris fault, and the position errors are evaluated by changing the size of the ephemeris faults that missed detection. It was confirmed that the existing fault monitors do not guarantee that the position error under the fault condition does not exceed the PL_H0. Further, the impact of the faults on the position errors is discussed.

• Journal of Positioning, Navigation, and Timing
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• 제6권4호
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• pp.195-203
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• 2017

A 2U cube satellite called SNUGLITE has been developed by GNSS Research Laboratory in Seoul National University. Its main mission is to perform actual operation by mounting dual-frequency global positioning system (GPS) receivers. Its scientific mission aims to observe space environments and collect data. It is essential for a cube satellite to control an Earth-oriented attitude for reliable and successful data transmission and reception. To this end, an attitude estimation and control algorithm, Attitude Determination and Control System (ADCS), has been implemented in the on-board computer (OBC) processor in real time. In this paper, the Extended Kalman Filter (EKF) was employed as the attitude estimation algorithm. For the attitude control technique, the Linear Quadratic Gaussian (LQG) was utilized. The algorithm was verified through the processor in the loop simulation (PILS) procedure. To validate the ADCS algorithm in the ground, the experimental verification via a single axis Hardware-in-the-loop simulation (HILS) was used due to the simplicity and cost effectiveness, rather than using the 3-axis HILS verification (Schwartz et al. 2003) with complex air-bearing mechanism design and high cost.

• 로봇학회논문지
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• 제18권1호
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• pp.72-81
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• 2023

We introduce tightly-coupled GNSS-LiDAR-Inertial state estimator, which is capable of SLAM (Simultaneously Localization and Mapping) and autonomous driving. Long term drift is one of the main sources of estimation error, and some LiDAR SLAM framework utilize loop closure to overcome this error. However, when loop closing event happens, one's current state could change abruptly and pose some safety issues on drivers. Directly utilizing GNSS (Global Navigation Satellite System) positioning information could help alleviating this problem, but accurate information is not always available and inaccurate vertical positioning issues still exist. We thus propose our method which tightly couples raw GNSS measurements into LiDAR-Inertial SLAM framework which can handle satellite positioning information regardless of its uncertainty. Also, with NLOS (Non-light-of-sight) satellite signal handling, we can estimate our states more smoothly and accurately. With several autonomous driving tests on AGV (Autonomous Ground Vehicle), we verified that our method can be applied to real-world problem.

• 한국항행학회논문지
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• 제20권1호
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• pp.37-44
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• 2016

한국은 성능기반항행 (PBN; performance-based navigation)으로 전환하기 위한 단계적 PBN 이행계획을 2010년에 수립하고 로드맵에 따른 새로운 비행절차를 개발 중에 있다. PBN 비행절차에는 GNSS (global navigation satellite systems), DME (distance measuring equipment), VOR (VHF omnidirectional range), INS (inertial navigation system) 등의 항행시설 (NAVAID; navigation aid)이 활용되는 것으로 되어있다. 그 중에서 GNSS를 이용한 PBN 업무제공이 중심을 이루고 있는 실정이다. 그러나 위성항법신호의 인위적, 자연적 간섭에 의한 취약성이 발견됨에 따라 세계 각국은 다양한 대안항법(APNT; alternative positioning, navigation and timing) 기술을 연구하고 있다. 본 논문에서는 GNSS 신호가 가용하지 않을 경우 기존의 항행시스템으로 지속적인 PBN 운항이 가능한지를 분석하였으며, 결과적으로 국내 일부 공항은 접근 단계 구역에서 대안항법의 구축이 필요한 것으로 나타났다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• 한국항행학회논문지
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• 제20권5호
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• pp.433-439
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• 2016

저가형 GNSS (global navigation satellite system) 수신 모듈에 DGNSS (differential GNSS) 서비스를 적용하기 위한 방안으로 거리 영역의 보정정보를 위치 영역으로 투영한 후, stand-alone으로 산출한 위치에 적용하는 DGNSS-CP 방식이 제안된 바 있다. DGNSS-CP 를 상용 수신기 또는 휴대폰에 적용하기 위해서는 항법 방정식의 관측행렬을 이용하여 위치영역 투영 방정식을 구성하므로, 각 위성의 시선벡터를 산출하여야 한다. GNSS 항법 메시지, 배치 정보 등이 시선벡터 산출을 위하여 사용되는데, 각 방법에 따라 정확도와 연산량 등의 성능에 차이가 발생한다. 본 연구에서는 제시된 두 가지 시선벡터 산출 방식에 따라 DGNSS-CP의 성능에 어떠한 영향을 끼치는지 확인하기 위하여, Septentrio PolaRx4 Pro 수신기에서 stand-alone mode 로 저장된 데이터에 해당 알고리즘을 적용하였고, 배치 정보를 사용하는 방법이 궤도정보를 사용하는 방법에 비해 정확도 면에서는 그 성능이 RMS (root mean square) 0.1 m 가량 저하되는 반면, 연산량은 약 1/15수준으로 줄일 수 있음을 확인하였다.

• Journal of Positioning, Navigation, and Timing
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• 제12권3호
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• pp.271-280
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• 2023

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.

• 한국위성정보통신학회논문지
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• 제5권2호
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• pp.69-74
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• 2010

본 논문에서는 LED 조명 기반의 실내 측위 시스템을 제안하였다. LED가 빛을 방출하며 고속의 switching이 가능하다는 반도체 조명이라는 특징을 이용하여 각각의 LED 조명에 고유의 8 비트 ID를 부여 후, 이를 방출되는 조명 빛에 변조하여 보내주었다. 수신기는 16 개의 LED 조명으로부터 조합된 정보를 수신하게 되고, 수신된 정보와 각각의 ID 정보 간의 correlation coefficient를 이용하여 $4\;m\;{\times}\;4\;m\;{\times}\;2\;m$의 공간에서 100 cm 및 50 cm resolution을 가지는 위치인식 시스템을 시뮬레이션을 통해 구현하여 보았다. 제안된 측위 시스템은 간단한 알고리즘을 사용하였고, LED 조명 인프라를 사용하여 구축함으로써 설치비용 절감이 가능할 것이라 기대된다.

• Journal of Positioning, Navigation, and Timing
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• 제12권1호
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• pp.1-9
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• 2023

The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.