• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2227-2232
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• 2013

To obtain the system requirement specification in the beginning of the precision guidance system development, the effectiveness and reliability analysis for the system are necessary. The main purpose of this research is to obtain the system requirement specification for the high speed unmanned underwater vehicles by carrying out the effectiveness analysis using the modeling and simulation scheme. The effectiveness is position error for target position. Reaching accuracy is expected to be affected by the navigation sensor parameter. Assume that the navigation sensors that is consist of inertial navigation system(INS) and doppler velocity log(DVL) is the parameter. To analyze the effectiveness of each parameter, Monte-Carlo numerical simulation is performed in this research. The effectiveness analysis is carried out using circular error probability(CEP) and variance analyze scheme. Considering the cost function, the specification of the navigation sensor is provided. The cost function is consist of the INS and DVL specification and the price of those sensors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.75-84
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• 2018

The purpose of this study is that the low-cost mobile mapping system using INS (Inertial Navigation System) based on MEMS (Micro Electro Mechanical System) could decipher the interpretation of road facility with the accuracy of x, y 0.546m plane error. Even though the MMS (Mobile Mapping System) technology as a new measurement technology has been used vividly to set up geographic information by some world leading surveying equipment manufacturers, the domestic technology is still in its beginning stage. Several domestic institutes and companies tried to catch up the leading technology but they just produced prototypes which needs more stabilization. Through this thesis, we developed low-cost mobile mapping system installed with INS based on MEMS after time synchronizing sensors for MMS such as LiDAR (Light Detection And Ranging), CCD (Charge Coupled Device), GPS/INS (Global Positioning System / Inertial Navigation System) and DMI (Distance Measurement Instrument).

• Journal of Advanced Navigation Technology
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• v.23 no.3
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• pp.221-227
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• 2019

In this paper, interacting multiple model (IMM) filter was designed that guarantees a stable navigation performance even in the unstable satellite navigation position. In order to design IMM filter in INS / GPS integrated navigation system, sub filter of the IMM filter is defined as Kalman filter. In the IMM filter configuration, two subfilters are determined. Each Kalman filter defines the six-teenth state composed of position, velocity, attitude, and sensor error from the INS error equation and the states additionally derived in case of the coloured measurement noise. In order to verify the performance of the proposed filter, we compared the performance how the filter works in the presence of arbitrary error in GPS navigation solution. The Monte Carlo simulation was performed 100 times and the results were compared with the root mean square(RMS). The results show that the proposed method is stable against errors and show fast convergence.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1589-1597
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• 2021

Due to the low output rate and time delay of vehicle's navigation results, the electro-optical tracking system(EOTS) cannot estimate accurate target positions. If an inertial measurement unit(IMU) is additionally mounted into the EOTS and inertial navigation system(INS) is constructed, the high navigation output rate can be obtained. And the time-delay can be compensated by using the augmented Kalman filter. An accurate initial attitude is required in order to have accurate navigation outputs. In this paper, an attitude determination algorithm is proposed using the augmented Kalman filter in order to compensate measurement delay of the EOTS and have accurate initial attitude. The proposed initial attitude determination algorithm consists of an augmented Kalman filter, an INS, and an integrated Kalman filter. The augmented Kalman filter compensates the time-delay of the vehicle's navigation results and the integrated Kalman filter estimates the navigation error of the INS. In order to evaluate performance of the proposed algorithm, vehicle's navigation outputs and IMU measurements were generated using sensors' model-based measurement generator and initial attitude estimation errors of the proposed algorithm and the conventional algorithm without the augmented Kalman filter were compared for the generated measurements. The evaluation results show that the proposed algorithm has better accuracy.

• Journal of Korean Society of Transportation
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• v.25 no.2 s.95
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• pp.157-165
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• 2007

In this research, a vertical alignment analysis algorithm was developed. The developed algorithm used acquired data from a vehicle with multiple sensors such as a global positioning system (GPS) an inertial navigation system (INS), and a distance measuring unit (DMI) to collect information about vehicle position and altitude. The vertical alignment analysis algorithm includes the identification of vertical tangent sections, the beginning and ending points of vertical curves, and the calculation of length of vortical curves. Also, the algorithm can help build models for vertical tangent sections and vertical curve sections. In order to verify the algorithm, a field survey was conducted at an actual highway section and the result of the field survey was compared to a highway CAD drawing.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.218-226
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• 2016

In the original design of K2 tank the Vertical Sensor Unit(VSU) was mounted to measure the attitude of the main gun to enhance the hit probability. In this research, as a part of efforts to reduce the cost of K2, it was theoretically simulated and evaluated to use the data from Inertial Navigation System(INS) for the calculation of the gun attitude instead of the direct measurement using VSU. It was found that the negative effect of INS approach is negligible and the elimination of VSU is technically possible and beneficial to the system.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.8
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• pp.698-705
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• 2002

The redundant sensor configuration problem of inertial navigation system(INS) is considered and analyzed the navigation and fault detection performance according to various sensor configurations. We considered various kinds of redundant sensor configurations for symmetric, cone, and orthogonal configurations and compare the navigation and fault detection performance for the configurations. We show that the navigation and fault detection performance is not affected by the coordinate change for a fixed configuration.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.265-270
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• 2006

Recently, inertial sensors like gyros and accelerometers have been quite miniaturized by Micro Electro-Mechanical Systems (MEMS) technology. JAXA is developing a MEM-based GPS/INS hybrid navigation system named Micro-GAIA. The navigation performance of Micro-GAIA was evaluated through off-line analysis by using flight test data. The estimation errors of the roll, pitch, and azimuth were $0.03^{\circ}$, $0.05^{\circ}$, $0.05^{\circ}$ $(1{\sigma})$, respectively. he horizontal position errors after 60-second GPS outages were reduced to 25 m CEP. The attitude errors and position errors are nearly half of ones reported previously[2]. Furthermore, using the adaptive Kalman filters, the robustness against the uncertainty of the measurement noise was improved. Comparing the innovation-based and residual-based adaptive Kalman filters, it was confirmed that the latter is robuster than the former.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.179-187
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• 2021

To estimate the location of the train, we consider an integrated navigation system that combines Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS). This system provides accurate navigation results in open sky by combining only the advantages of both systems. However, since measurement update cannot be performed in GNSS signal blocked areas such as tunnels, mountain, and urban areas, pure INS is used. The error of navigation information increases in this area. In order to reduce this problem, the train's Non-Holonomic Constraints (NHC) information can be used. Therefore, we deal with the INS/GNSS/NHC integrated navigation system in this paper. However, in the process of installing the navigation system on the train, a Mounting Misalignment Error of the IMU (MMEI) inevitably occurs. In this case, if the NHC is used without correcting the error, the navigation error becomes even larger. To solve this problem, a method of easily estimating the MMEI without an external device is introduced. The navigation filter is designed using the Extended Kalman Filter (EKF) by considering the MMEI. It is assumed that there is no vertical misalignment error, so only the horizontal misalignment error is considered. The performance of the integrated navigation system according to the presence or absence of the MMEI and the estimation performance of the MMEI according to the method of using NHC information are analyzed based on simulation. As a result, it is confirmed that the MMEI is accurately estimated by using the NHC information together with the GNSS information, and the performance and reliability of the integrated navigation system are improved.

• Journal of Sensor Science and Technology
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• v.16 no.4
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• pp.294-300
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• 2007

Autonomous unmanned vehicle is able to find the path and the way point by itself. For the more precise navigation performance, Extended kalman filter, which is integrated with inertial navigation system and global positioning system is proposed in this paper. Extended kalman filter's performance is evaluated by the simulation and applied to the unmanned vehicle. The test result shows the effectiveness of extended kalman filter for the navigation.