• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1279-1286
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• 1999

In conventional GPS/DR integration schemes, the GPS position (or pseudo-range) information is used in calibrating DR sensors. In those schemes, however, an inaccurate calibration may degrade the position accuracy when the GPS measurement is not available. This paper presents a new integration scheme where the GPS velocity information is used in calibrating DR sensors. Also proposed is a new error model of DR sensors for calibrating the bias error and the tilt error in dynamic environments. The proposed model makes it possible that the errors of both the DR sensor parameters and the velocity are calibrated using the GPS carrier-based velocity(or the pseudo-range rate) measurement while the DR position error is calibrated using the GPS position measurement. Since the DR sensors are calibrated accurately, the positioning accuracy is drastically improved when the GPS measurements are unavailable.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.944-952
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• 2001

Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it is desired to combine INS with external aids such as GPS. However GPS informations have a randomly abrupt jump due to a sudden corruption of the received satellite signals and environment, and moreover GPS can\`t provide navigation solutions. In this paper, smoothing and prediction schemes are proposed for GPS`s jump or unavailable GPS. The smoothing algorithm which is designed as a scalar adaptive filter, smooths abrupt jump. The prediction algorithm which is proved by Schuler error model of INS, estimates INS error in appropriate time. The outputs of proposed algorithm apply stable measurements to GPS aided INS Kalman filter. Simulations show that the proposed algorithm can effectively remove measurement jump and predict INS error.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1067-1075
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• 2002

The system error model for the compensation of the low-cost personal navigation system is derived and the error compensation method using GPS is also proposed. The walking navigation system (WNS) that calculates navigation information through walking detection has small error than INS, but the error also increases with time. In order to improve reliability of the system regardless of time, WNS is integrated with GPS. Since WNS is usually used in urban area, the blockage of CPS signal is frequently occurred. Therefore tightly coupled Kalman filter is used for the integration of WNS and GPS. In this paper, the system model for the design of tightly coupled Kかm filter is designed and measurement is linearized in consideration of moving distance error. It is shown by Monte Carlo simulation that the error is bounded even through the number of visible satellite is less than 4.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.221-227
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• 2002

In this paper, the system model for the compensation of the low-cost personal navigation system is derived and the error compensation method using GPS is also proposed. WNS(Walking Navigation System) is a kind of personal navigation system using the number of a walk, stride and azimuth. Because the accuracy of these variables determines the navigation performance, computational methods have been investigated. The step is detected using the walking patterns, stride is determined by neural network and azimuth is calculated with gyro output. The neural network filters off unnecessary motions. However, the error compensation method is needed, because the error of navigation information increases with time. In this paper, the accumulated error due to the step detection error, stride error and gyro bias is compensated by the integrating with GPS. Loosely coupled Kalman filter is used for the integration of WNS and GPS. It is shown by simulation that the error is bounded even though GPS signal is blocked.

• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.205-215
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• 2012

A GPS sensor is widely used in many areas such as navigation, or air traffic control. Particularly, the car navigation system is equipped with GPS sensor for locational information. However, when a car goes through a tunnel, forest, or built-up area, GPS receiver cannot get the enough number of satellite signals. In these situations, a GPS receiver does not reliably work. A GPS error can be formulated by sum of bias error and sensor noise. The bias error is generated by the geometric arrangement of satellites and sensor noise error is generated by the corrupted signal noise of receiver. To enhance GPS sensor accuracy, these two kinds of errors have to be removed. In this research, we make the road database which includes Road Database File (RDF). RDF includes road information such as road connection, road condition, coordinates of roads, lanes, and stop lines. Among the information, we use the stop line coordinates as a feature point to correct the GPS bias error. If the relative distance and angle of a stop line from a car are detected and the detected stop line can be associated with one of the stop lines in the database, we can measure the bias error and correct the car's location. To remove the other GPS error, sensor noise, the Kalman filter algorithm is used. Additionally, using the RDF, we can get the information of the road where the car belongs. It can be used to help the GPS correction algorithm or to give useful information to users.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.2
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• pp.11-18
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• 2016

Global Positioning System (GPS) is currently widely used for aviation applications. Single-frequency GPS receivers are highly affected by the ionospheric delay error, and the ionospheric delay should be corrected for accurate positioning. Single-frequency GPS receivers use the Klobuchar model, whose model parameters are transmitted from GPS satellites. In this paper, the long-term accuracy of the Klobuchar model from 2002 to 2014 is analyzed. The IGS global ionosphere map is considered as true ionospheric delay, and hourly, seasonal, and geographical error variations are analyzed. Histogram of the ionospheric delay error is also analyzed. The influence of solar and geomagnetic activity on the Klobuchar model error is analyzed, and the Klobuchar model error is highly correlated with solar activity. The results show that the Klobuchar model estimates 8 total electron content unit (TECU) over the true ionosphere delay in average. The Klobuchar model error is greater than 12 TECU within $20^{\circ}$ latitude, and the error is less than 6 TECU at high latitude.

• Journal of Navigation and Port Research
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• v.34 no.7
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• pp.511-516
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• 2010

GPS anomaly has increased according to the degradation of satellite performance, and many GPS users could be exposed to any kinds of error-included signals without any previous notice when unscheduled error occurred. RSIM (Reference Station Integrity Monitors) is a typical monitoring method to broadcast PRC (Pseudo Range Correction) for users. However, there were some cases that the receiver detected the anomalous satellite's signal even though it was unhealthy set, consequently it occurred a large range error. Then it is important to monitor the integrity of GPS signal and it is needed to devise the correction method of pseudorange by eliminating error-occurred PRN for notification to GPS users when it is monitored that the anomaly occurred. This paper proposes the basic concept of how to correct the pseudorange. The paper also shows the analysis results of PRN10 GPS anomaly occurred on day 39 in 2007 with corrected results by eliminating anomaly satellite (PRN10). The proposed correction method shows decreased pseudorange error range compared to the case when the anomaly satellite were used.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2223-2229
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• 2010

The integrity monitoring of anomalous GPS signal have been researched because of the degradation of GPS satellite performance. It is known that anomalous GPS signal can occur by failure of GPS satellite, sudden increase of ionosphere delay error, SA, wrong modeling for navigation parameters from control segment, and an electromagnetic wave interference, etc. In case of GPS anomaly by satellites can arise from carrier frequency, code and navigation message. In this paper, the scenarios with navigation message errors were made by using GPS simulator, and the influences of GPS navigation message error to receiver were analysed. The anomalies of preamble, bits related TOW count message, subframe ID in HOW, bits related satellite healthy, and the other navigation message errors were described and simulated. Also, the number of satellites, DOP and pseudorange are analyzed to know how the anomalous signal can affect on GPS receiver.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.1 s.24
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• pp.5-12
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• 2006

This study describes an SDINS/GPS/ZUPT integration algorithm for land navigation systems. The SDINS error can be decoupled in two parts. The first part is the the Schuler component which does not depend on object motion parameters, and the other is the Non-Schuler part which depends on the product of object acceleration and azimuth error. Azimuth error causes SDINS error in proportion to the traversed distance. The proposed system consists of a GPS/SDINS integration system and an SDINS/ZUPT integration system, which are both realized by an indirect feedforward Kalman filter. The main difference between the two is whether the estimate includes the Non-Schuler error or not, which is decided by the measurement type. Consequently, subtracting GPS/SDINS outputs from SDINS/ZUPT outputs provide the Non-Schuler error information which can be applied to improving azimuth accuracy. Simulation results using the raw data obtained from a van test attest that the proposed SDINS/GPS/ZUPT system is capable of providing azimuth improvement.