• 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.11 no.8
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• pp.717-725
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• 2005

The adaptive filter is proposed for the INS/GPS. The proposed filter can estimate the variance of the process noise using the residual of the filter. To verify the efficiency of the adaptive filter, it is applied to the loosely-coupled INS/CPS that employs the additive quaternion error model. Simulation results demonstrate that the proposed filter is more effective in estimating the attitude error than EKF.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.3
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• pp.165-173
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• 2018

Underwater vehicles use Inertial Navigation System (INS) with high-performance Inertial Measurement Unit (IMU) for high precision navigation. However, when underwater navigation is performed for a long time, the INS error gradually diverges, therefore, an integrated navigation method using auxiliary sensors is used to solve this problem. In terms of underwater vehicles, the vertical axis error is primarily compensated through Vertical Channel Damping (VCD) using a depth gauge, and an integrated navigation filter can be designed to perform horizontal axis error and sensor error correction using a speedometer such as Electromagnetic-Log (EM-Log). However, since EM-Log outputs the forward direction relative speed of the vehicle with respect to the sea and sea current, INS correction filter using this may cause a rather large error. Although it is possible to design proper filters if the exact model of the sea current is known, it is impossible to know the accurate model in reality. Therefore, this study proposes an INS/EM-Log integrated navigation filter with the function to estimate sea current using an Interacting Multiple Model (IMM) filters, and the performance of this filter is analyzed through a simulation performed in various environments.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1144-1148
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• 1996

This paper presents WMR modelling and path tracking algorithm using Inertial Navigation System. The error models of gyroscope and accelerometers in INS are derived by Gauss-Newton method which is nonlinear regression model. Then, to test availability of error model, we pursue the fitness diagnosis about probability characteristic for real data and estimated data. Performance of inertial sensor with error model and Kalman filter is pursued by comparing with one without them. The computer simulation shows that position error remarkably decrease when error compensation is applied.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.990-994
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• 2003

When the low cost IMU is used, the result of the stationary self alignment is not suitable for navigation. In this paper, an in-motion alignment method is proposed to obtain an accurate initial attitude of a low cost IMU based GPS/INS integration system. To design Kalman filter for alignment, large heading error model is introduced. And then Kalman filter is designed to estimate initial attitude error as the indirect feedback filter. In order to assess performance of the alignment method, computer simulations are carried out. The simulation results show that initial attitude error rapidly reduces.

• 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 Institute of Control, Robotics and Systems
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• v.14 no.6
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• pp.595-602
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• 2008

In this paper, performance of the initial alignment for INS whose attitude is not leveled is investigated. Observability of the initial alignment filter is analyzed and estimation errors of the estimated state variables are derived. First, the observability is analyzed using the rank test of observability matrix and the normalized error covariance of the Kalman filter based on the 10-state model. In result, it can be seen that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and the non-leveling tilt angles of a vehicle containing the INS. Especially, this paper shows that the larger the tilt angles of the vehicle are, the larger the estimation errors corresponding to the sensor biases are. Finally, it is shown that the performance of the 8-state model excepting the accelerometer biases on horizontal axes is better than that of the 10-state model in the initial alignment by simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.584-591
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• 2014

This paper presents a novel aided navigation method for AUV (Autonomous Underwater Vehicles). The navigation system for AUV includes several sensors such as IMU (Inertial Measurement Unit), DVL (Doppler Velocity Log) and depth sensor. In general, the $13^{th}$ order INS error model, which includes depth error, velocity error, attitude error, and the accelerometer and gyroscope biases as state variables is used with measurements from DVL and depth sensors. However, the model may degrade the estimation performance of the heading state. Therefore, the $11^{th}$ INS error model is proposed. Its validity is verified by using a degree of observability and analyzing steady state error. The performance of the proposed model is shown by the computer simulation. The results show that the performance of the reduced $11^{th}$ order error model is better than that of the conventional $13^{th}$ order error model.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.72-78
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• 2007

Determination of the local vertical is not trivial for a moving vehicle and in general will require corrections for the Earth geophysical deflection. The vehicle's local vertical can be estimated by INS integration with initial alignment in SDINS(Strap Down INS) system. In general, the INS has drift error and it cause the performance degradation. In order to compensate the drift error, GPS/INS augmented system is widely used. And in the event that GPS is denied or unavailable, celestial navigation using star tracker can be a backup navigation system especially for the military purpose. In this celestial navigation system, the vehicle's position determination can be achieved using more than two star trackers, and the accuracy of position highly depends on accuracy of local vertical direction. Modern tilt sensors or accelerometers are sensitive to the direction of gravity to arc second(or better) precision. The local gravity provides the direction orthogonal to the geoid and, appropriately corrected, toward the center of the Earth. In this paper the relationship between direction of center of the Earth and actual gravity direction caused by geophysical deflection was analyzed by using precision orbit simulation program embedded the JGM-3 geoid model. And the result was verified and evaluated with mathematical gravity vector model derived from gravitational potential of the Earth. And also for application purpose, the performance variation of pure INS navigation system was analyzed by applying precise gravity model.