• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.6
• /
• pp.677-685
• /
• 2011

In order to enhance public transportation and to maintain information credibility, improvement of accuracy regarding route and positional information of public transport is very significant. There have been a variety of methods using GPS to measure accuracy of location-based services. However, the researches of evaluation regarding kinematic position of linear objects measured by vehicle/kinematic GPS are still insufficient. That's why our paper aims to suggest method of evaluation accuracy on a real-time bus route surveyed by GPS by SBM(Single Buffering Method). To make it come true, we compared the findings on the static and dynamic positioning by using PP(Point Positioning), DGPS and GPS/INS integrated systems and analyzed the accuracy and error effects among them, focusing on Anyang city. Consequently, we can find out that in case of P.P. comparing positioning accuracy between RTK DGPS and GPS/INS, both of them have survey result within a margin of error of 5m. More importantly, we can evaluate positional accuracy of each GPS system based on the work provision of a public survey such as error for P.P.(14.5m, 18.1m), DGPS(16.9m, 18.5m), and GPS/INS(18.4m, 18.5m). We are expecting that proposed method in our paper can be utilized in a wide range of categories such as feasibility testing of GPS field survey and high accuracy of positioning for Bus Information System.

• Journal of Positioning, Navigation, and Timing
• /
• v.1 no.1
• /
• pp.45-49
• /
• 2012

The flight performance evaluation of navigation system is very significant because the reliability of navigation data directly affect the safety of aircraft. Especially, the high-level navigation system such as DGPS/INS, need more precise flight performance evaluation method. The performance analysis is evaluated by comparing between the navigation system in aircraft and reference trajectory which is more precise than navigation system in aircraft. In order to verify DGPS/INS performance of m-level, the GPS receiver, which is capable post-processed Carrier-phase Differential GPS(CDGPS) method of cm-level, have to be used as reference system. The DGPS/INS is estimated the Center of Gravity (CG) point of aircraft to offer precise performance while the reference system is output the position of GPS antenna which is mounted on the outside of aircraft. Therefore, in order to more precise performance evaluation, it needs to compensate the lever arm and coordinates transformation. This paper use quaternion and Direct Cosine Matrix(DCM) methods as coordinate transformation matrix in lever arm compensation of CDGPS reference trajectory. And it compares NED errors of DCM and quaternion transformation in lever arm of reference trajectory via DGPS/INS result.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.2
• /
• pp.225-234
• /
• 2009

Real-time Aerial Monitoring System (RAMS) is to perform the rapid mapping in an emergency situation so that the geoinformation such as orthophoto and/or Digital Elevation Model is constructed in near real time. In this system, the GPS/INS plays an very important role in providing the position as well as the attitude information. Therefore, in this study, the performance of an IMU sensor which is supposed to be installed on board the RAMS is evaluated. And the integration algorithm of GPS/INS are tested with simulated dataset to find out which is more appropriate in real time mapping. According to the static and kinematic results, the sensor shows the position error of 3$\sim$4m and 2$\sim$3m, respectively. Also, it was verified that the sensor performs better on the attitude when the magnetic field sensor are used in the Aerospace mode. In the comparison of EKF and UKF, the overall performances shows not much differences in straight as well as in curved trajectory. However, the calculation time in EKF was appeared about 25 times faster than that of UKF, thus EKF seems to be the better selection in RAMS.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.12
• /
• pp.1284-1289
• /
• 2014

In this Paper, outdoor position estimation system was implemented using GPS (Global Positioning System) and INS (Inertial Navigation System). GPS position information has lots of errors by interference from obstacles and weather, the surrounding environment. To reduce these errors, multiple GPS system is used. Also, the Discrete Wavelet Transforms was applied to INS data for compensation of its error. In this paper, position estimation of the mobile robot in the straight line is conducted by EKF (Extended Kalman Filter). However, curve running position estimation is less accurate than straight line due to phase change in rotation. The curve is recognized through the rate of change in heading angle and the position estimation precision of the initial curve was improved by UPF (Unscented Particle Filter). In the case of UPF, if the number of particle is so many that big memory gets size is needed and processing speed becomes late. So, it only used the position estimation in the initial curve. Thereafter, the position of mobile robot in curve is estimated through switching from UPF to EKF again. Through the experiments, we verify the superiority of the system and make a conclusion.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.277-282
• /
• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.974-977
• /
• 2003

The GPS/INS integration system navigation can provide improved navigation performance and has been widely used as a main navigation system for military and commercial vehicles. When two navigation systems are tightly coupled and the structure is complicated, a fault in either the GPS or the INS can lead to a disastrous failure of the whole integration system. This paper proposes a real-time fault detection method for an AGPS/INS integration system. The proposed fault detection method comprises a BIT and a fault detection algorithm based on chi-square test. It is implemented by real-time software modules to apply the AGPS/INS integration system and van test is carried out to evaluate its performance.

• The Journal of Korea Robotics Society
• /
• v.8 no.2
• /
• pp.129-135
• /
• 2013

In this paper, a high precision outdoor positioning system is newly proposed using multiple GPS receivers based on the Extended Kalman Filter (EKF). Typically, the GPS signal has the instantaneous errors that degrade the positioning seriously. Using the multiple GPS receivers instead of an expensive DGPS receiver, the positioning reliability and accuracy are improved in this research as a low cost solution. To incorporate the small displacement, an INS data have been tightly coupled to the GPS data, which has the inherit disadvantage of the cumulative error occurring over time. To achieve a stabilized and accurate positioning system, the multiple GPS receiver data are fused with the INS data through the EKF process. Through real navigation experiments of an outdoor mobile robot, the performance of the proposed system has been verified to be accurate comparable to DGPS system with a lower cost.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.2
• /
• pp.247-254
• /
• 2006

For more accurate navigation, lever arm compensation is considered. The compensation method for GPS and an odometer is introduced and new compensation methods are proposed for an odometer to consider the effect of coordinate transformation errors and the scale factor error. The methods are applied to a GPS/INS/odometer integrated system and the simulation and experimental results show its effectiveness.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.53.1-53
• /
• 2002

$\textbullet$ Contents 1. Introduction $\textbullet$ Contents 2. Error Models of Navigation $\textbullet$ Contents 3. Observablity Properties of GPS/INS $\textbullet$ Contents 4. Measurement System for INS Error Estimation $\textbullet$ Contents 5. Numerical Simulation Results $\textbullet$ Contents 6. Conclusions