• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2641-2647
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• 2003

Conventional DLLs estimate the delay times of satellite signals individually and feed back these measurements to the VCO independently. But VDLL estimates delay times and user position directly and then estimate the feedback term for VCO using the estimated position changes. In this process, input measurements are treated as vectors and these vectors are used for navigation. First advantage of VDLL is that noise is reduced in all of the tracking channels making them less likely to enter the nonlinear region and fall below threshold. Second is that VDLL can operate successfully when the conventional independent parallel DLL approach fails completely. It means that VDLL receiver can get enough total signal power to track successfully to obtain accurate position estimates under the same conditions where the signal strength from each individual satellite is so low or week that none of the individual scalar DLL can remain in lock when operating independently. To operate VDLL successfully, it needs to know the initial user dynamics and position and prevents total system from the divergence. The suggested integration method is to use the inertial navigation system to provide initial dynamics for VDLL and to maintain total system stable. We designed the GPS/INS integrated navigation system. This new type of integrated system contained the vector pseudorange format generation block, VDLL signal processing block, position estimation block and the conversion block from position change to delay time feedback term aided by INS.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2588-2590
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• 2005

This paper presents an three dimension attitude of inertial navigation system(INS) for managing a transporter in shipyard by using low-cost inertial sensors. The GPS(Global Positioning System) shade field prevents from receiving information of position through GPS satellites, GIS(Geographic Information System) in shipyard, therefore TNS system plays an important part. This system is composed of tiny low-cost gyroscopes, accelerometers and a magnetic compass, and 3-dimension position is estimated by an indirect Kalman filter using the outputs of these inertial sensors.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.197-206
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• 2019

Safe aircraft requires highly reliable navigation information. The traditionally used inertial navigation system (INS) often displays faulty location information due to its innate errors. To overcome this, the INS/GNSS or INS/TRN integrated navigation can be used. However, GNSS is vulnerable to jamming and spoofing, while TRN can be degraded in the flat and repetitive terrains. In this paper, to improve the performance and ensure the high reliability of the navigation system, the INS/GNSS/TRN integrated navigation based on federated filter is designed. Master filter of the integrated navigation uses the estimates and covariances of two local filters - INS/GNSS and INS/TRN integrated filters. The local filters are designed with the EKF that is feedforward type and composed of the 17st state variables. And the INS/GNSS integrated navigation includes the barometer error compensation method. Finally, the proposed INS/GNSS/TRN integrated navigation is verified by vehicle and captive flight tests.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.235-243
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• 2022

In this paper, in the case of an inertial navigation system, the posture estimation error in the initial alignment due to vertical deflection is analyzed. Posture estimation error due to DOV was theoretically analyzed based on the speed and posture error of INS. Simulations were performed to verify the theoretical grinding, and the results were in good agreement. For example, in the case of η=20", an alignment error of ϕ_N=0.00287°, ϕ_U=0.00196° occurred, and in the case of 𝜉=20", an error of ϕ_E= -0.00286° occurred. Through this, it was confirmed that the vertical posture error caused by the DOV occurred as a coupling characteristic of the INS posture error. It has been shown that an additional posture error may occur due to the DOV, which was not considered in the existing INS alignment, which means that correction for the DOV must be considered when applying high-precision INS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2520-2527
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• 2015

Position information is important to carry out military operations. In general, GPS is used to estimate position. However, GPS is vulnerable to jamming due to the low received signal strength, therefore GPS can be easily jammed. The relative navigation is an auxiliary navigation system defined in JTIDS. When GPS is jammed, the relative navigation requires ground reference units on the ground to operate accurately. If the ground reference unit does not exist, nodes operated by the relative navigation depend on the inertial navigation system to identify their position. However, this positioning scheme based on only INS causes accumulative position error, therefore the nodes cannot identify their position accurately for a long time. In this paper, we propose an alternative to reduce position error generated by depending inertial navigation system. In order to verify that the performance of proposed scheme is better than that of the existing scheme, various simulations are conducted.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.8
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• pp.767-773
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• 2009

This research have introduced wavelet signal processing technic for improving navigation signals. INS signals can be distorted with conventional pre-filtering method such as low-pass filtering by unwanted smoothing on real signals. But in this paper, wavelet thresholding method is implemented to INS signal to denoise for INS-GPS integrated system. This method reduces signal noise but not distorts the rapid varing signal. And this paper applied thresholding to INS-GPS integrated navigation system and improved navigation performance.

• 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.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2433-2435
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• 2000

Inertial Navigation System(INS) provides short-term accurate navigation solution but its error grows with time due to integration characteristics. Meanwhile, Global Positioning System(GPS) provides long-term stable solution but it has poor error characteristics in high dynamic region. So for its synergistic relationship, an integrated INS/GPS systems has been widely used as an advanced navigation system. Generally, two kinds of integration method are used. One is loosely coupled mode which uses GPS-derived position and velocity as measurements in an integrated Kalman filter. The other is tightly coupled one which uses pseudorange and pseudorange rate as Kalman filter measurements. In this paper the system error models and observation models for two kinds of integrated systems are derived, respectively, and their performance are compared through Monte-Carlo simulations.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.46-55
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• 2006

The purpose of this study is a development of an automatic mosaicing for applying to large number of airborne multispectral images, which reduces manual operation by human. 2436 airborne multispectral images were acquired from DuncanTech MS4100 camera with three bands; green, red and near infrared. LIDAR(LIght Detection And Ranging) data and GPS/INS(global positioning system/inertial navigation system) data were collected with the multispectral images. First, the multispectral images were converted to image patterns by unsupervised classification. Their patterns were compared with those of adjacent images to derive relative spatial position between images. Relative spatial positions were derived for 80% of the whole images. Second, it accomplished an automatic mosaicing using GPS/INS data and unsupervised classification. Since the time of GPS/INS data did not synchronized the time of readout images, synchronized GPS/INS data with the time of readout image were selected in consecutive data by comparing unsupervised classified images. This method realized mosaicing automatically for 96% images and RMSE (root mean square error) for the spatial precision of mosaiced images was only 1.44 m by validation with LIDAR data.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.239-247
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• 2022

The existing studies on the localization in the GNSS (Global Navigation Satellite System) denied environment usually exploit low-cost MEMS IMU (Micro Electro Mechanical Systems Inertial Measurement Unit) sensors to replace the GNSS signals. However, the navigation system still requires GNSS signals for the normal environment. This paper presents an integrated GNSS/INS (Inertial Navigation System) navigation system which combines GNSS and multiple IMU sensors using extended Kalman filter in partially GNSS-denied environments. The position and velocity of the INS and GNSS are used as the inputs to the integrated navigation system. The Mahalanobis distance is used for novelty detection to detect the outlier of GNSS measurements. When the abnormality is detected in GNSS signals, GNSS data is excluded from the fusion process. The performance of the proposed method is evaluated using MATLAB/Simulink. The simulation results show that the proposed algorithm can achieve a higher degree of positioning accuracy in the partially GNSS-denied environment.