• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.106-110
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• 2002

4S-Van is being developed in order to provide the spatial data rapidly and accurately. 4S-Van technique is a system for spatial data construction that is heart of 4S technique. Architecture of 4S-Van system consists of hardware integration part and post-processing part. Hardware part has GPS, INS, color CCD, camera, B/W CCD camera, infrared rays camera, and laser. Software part has GPS/INS integration algorithm, coordinate conversion, lens correction, camera orientation correction, and three dimension position production. In this paper, we suggest that adequate 4S-Van design is needed according to application environment from various test results.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.822-825
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• 1999

This paper compares two methods of GPS/INS integration ; tightly-coupled integration ana loosely-coupled integration. In the tightly -coupled method an integrated Kalman filter is designed to process raw GPS measurement data for state update and INS data for propagation. The loosely-coupled integration method uses the solution outputs from a stand-alone GPS receiver for update. The loosely-coupled method is simpler and can readily be applied to off-the-self receivers and sensors while the tightly-coupled integration requires access to raw measurement mechanism of the receiver. Simulation result show that the tightly-coupled integration system exhibits better performance and robustness than loosely-coupled integration method.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.3
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• pp.302-308
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• 2014

In this paper, a detailed investigation of the random vibration test is presented for strapdown inertial navigation systems (INS). The effect of the random vibration test has been studied from the point of view of navigation performance. The role of distribution functions and RMS value is represented to determine a feasible method to reject or reduce vibration related error in position and velocity estimation in inertial navigation. According to a survey conducted by the authors, this is the first time that the effect of the distribution function in vibration related error has been investigated in random vibration testing of INS. Recorded data of navigation grade INS is used in offline static navigation to examine the effect of different characteristics of random vibration tests on navigation error.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.817-823
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• 2007

GPS and INS can be integrated in 3 ways of loose, tight, and ultra-tight configuration. This paper investigates the performance of GPS/INS ultra-tightly integrated system when unscented Kalman filter(UKF) is adopted as well as extended Kalman filter(EKF). Covariance analysis is performed using UFK and EKF for tightly coupled and ultra-tightly coupled systems. Various trajectories such as straight, circle, S-shape, spiral are considered for the simulations of covariance analysis.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.4
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• pp.116-123
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• 2008

In this paper we study the performance of the measurement time-delay estimation of tightly-coupled GPS/INS(Global positioning system/Inertial Navigation system) system. Generally, the heading error estimation performance of loosely-coupled GPS/INS system using GPS's Navigation Solution is poor. In the case of tightly-coupled GPS/INS system using pseudo-range and pseudo-range rate, the heading error estimation performance is better. However, the time-delay error on the measurement(pseudo-range rate) make the heading error estimation performance degraded. So that, we propose the time-delay model on the measurement and compose the time-delay estimator. And we confirm that the heading error estimation performance in the case of measurement time-delay existence is similar with the case of no-delay by Monte-Carlo simulation.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.1
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• pp.29-34
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• 2022

In this paper, we analyze the Non-Holonomic Constraint (NHC) satisfaction of Inertial Navigation System (INS) for vehicular navigation according to Inertial Measurement Unit (IMU) location. In INS-based vehicle navigation, NHC information is widely used to improve INS performance. That is, the error of the INS can be compensated under the condition that the velocity in the body coordinate system of the vehicle occurs only in the forward direction. In this case, the condition that the vehicle's wheels do not slip and the vehicle rotates with the center of the IMU must be satisfied. However, the rotation of the vehicle is rotated by the steering wheel which is controlled based on the Ackermann geometry, where the center of rotation of the vehicle exists outside the vehicle. Due to this, a phenomenon occurs that the NHC is not satisfied depending on the mounting position of the IMU. In this paper, we analyze this problem based on Ackermann geometry and prove the analysis result based on simulation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.6
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• pp.481-487
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• 2013

In GPS(Global Positioning System)/INS(Inertial Navigation System) integrated navigation systems, GPS antennas and an inertial measurement units are usually installed outside and inside of the vehicle, respectively. By the difference of installed locations, performance of GPS/INS integrated navigation systems is affected by lever arm errors. For more accurate navigation, lever arm errors need to be compensated correctly since it directly affects the accuracy of navigation states. This paper proposes an effective lever arm error compensation method that utilizes velocity measurements of GPS and INS. By an experiment, feasibility of the proposed algorithm is verified. It is also shown that lever arm compensation is especially important when vehicles are experiencing rotational movements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.6
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• pp.63-70
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• 2024

Positioning technology is essential for the safe operation of moving objects in real-time, such as autonomous vehicles and UAVs. While traditional GPS/INS fusion positioning systems offer high accuracy, their performance degrades in GPS shadow zone due to the error accumulation problem of the INS. To address this issue, a GPS/INS fusion positioning system based on CNN-GRU is proposed in this paper. The proposed system predicts the position of the moving object in GPS shadow zone using a CNN-GRU model and inputs the predicted values into a Kalman filter to estimate the position with high accuracy even in GPS shadow zone. In order to validate the performance of the proposed model, training and simulations were performed on the North Campus Long-Term (NCLT) dataset. Experimental results show that the proposed positioning system reduced the positioning error in GPS shadow zones by up to 83.3% compared to the positioning results using only the traditional Kalman filter.

• Spatial Information Research
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• v.10 no.3
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• pp.407-419
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• 2002

In this study, the design of 4S-Van system is discussed fur the implementation of laser mapping system. Laser device is fast and accurate sensor that acquires 3D road and surface data. The orientation laser sensor is determined by loosely coupled (D)GPS/INS Integration. Considering current system architecture, (D)GPS/INS integration is performed far performance analysis of direct georeferencing and self-calibration is performed for interior and exterior orientation and displacement. We utilized 3 laser sensors for compensation and performance improvement. 3D surface data from laser scanner and texture image from CCD camera can be used to implement 3D visualization.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.286-289
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• 1986

항법(navigation)은 기준좌표계에 대한 항체(vehicle)의 위치나 속도를 알아내기 위한 것으로 이를 위한 시스템이 관성항법장치(inertial navigation system-INS)이며 항법기능을 수행하기 위하여 항체에 놓여진 쎈서의 관성성질을 이용한다. INS는 specific force와 관성 각속도의 측정에서 얻은 데이타를 처리함으로 그 기능을 수행한다. 스트랩다운 INS(SINS)는 관성항법장치의 한 종류로 analytic INS라고도 하는데 기준좌표축을 유지하기 위하여 안정테이블을 사용하지 않고 쎈서들을 항체에 직접 부착시켜 초기상태와 현재상태와의 사이에 상대적인 회전방향을 해석적으로 계산한다. INS의 성능은 수많은 오차원(error source)의 함수로 주어지며 이 오차원 중에는 주위환경에 의한 것도 있고 INS 구성에 사용된 기구(instruments)와 관련된 것도 있다. INS 를 해석하는 목적은 항법의 정확도를 알아보는데 있으며 또한 각각의 오차원의 값을 추정하는 것도 부가적인 목적이 된다. 이러한 오차의 추정치는 사양(specification)을 모르는 부품의 성능을 식별하는데 사용될 수 있다. 따라서 INS를 해석함으로 INS를 구성하는 어떤 부품에 대한 성능이 어느정도 개선을 필요로 하는가 알 수 있다. 본 논문에서는 SINS의 오차원을 크게 고도계의 불확실성, 중력의 편향과 이상, 가속도계의 불확실성, 자이로의 불확실성의 네 그룹으로 나누어 상호분산해석(covariance analysis)방법으로 각 오차원이 시스템에 미치는 영향을 알아보았다.