• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.26-26
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• 2000

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using robust H$_2$ filter. We suggest an uncertainty model for INS and apply the suggested discrete robust H$_2$ filter to the uncertainty model compared with kalman filter, the discrete robust H$_2$ filter is shown by simulation to have good performance of alignment time and accuracy.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1524-1529
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• 2003

In this paper deals with a unique method for measuring vehicle states such as body sideslip angle and tire sideslip angle using GPS velocity information in conjunction with other sensors. A method for integrating Inertial Navigation System (INS) sensors with GPS measurements to provide higher update rate estimates of the vehicle states is presented, and the method can be used to estimate the tire cornering stiffness. The experimental results for the GPS velocity-based sideslip angle measurement and cornering stiffness estimates are compared with the theoretical predictions. From the experimental results, it can be concluded that the proposed method has an advantage for future implementation in a vehicle safety system.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1243-1251
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• 2007

In this paper a study on the simplified observability analysis of GPS/INS is introduced. Errors for the position, velocity, attitude, gyro and accelerometer biases, and lever arm between GPS antenna and inertial sensors are considered in the observablity analysis. From the error dynamics model in which relatively small terms are neglected, simple observability conditions are obtained such that the observability of GPS/INS is determined by the test on the attutude, gyro bias, and lever arm. Unobservable errors for the position, velocity, and accelerometer bias are determined by those for the attitude, gyro bias, and lever arm. The simplified observability conditions are applied to a constant speed horizontal motion. It is shown that there are seven unobservable modes for the motion including the vertical component of gyro bias. The analytic observability analysis results are confirmed with a covariance simulation.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.3
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• pp.626-637
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• 2014

On ship, especially on large ship, the flexure deformation between Master (M)/Slave (S) Inertial Navigation System (INS) is a key factor which determines the accuracy of the integrated system of M/S INS. In engineering this flexure deformation will be increased with the added ship size. In the M/S INS integrated system, the attitude error between MINS and SINS cannot really reflect the misalignment angle change of SINS due to the flexure deformation. At the same time, the flexure deformation will bring the change of the lever arm size, which further induces the uncertainty of lever arm velocity, resulting in the velocity matching error. To solve this problem, a $H_{\infty}$ algorithm is proposed, in which the attitude and velocity matching error caused by deformation is considered as measurement noise with limited energy, and measurement noise will be restrained by the robustness of $H_{\infty}$ filter. Based on the classical "attitude plus velocity" matching method, the progress of M/S INS information fusion is simulated and compared by using three kinds of schemes, which are known and unknown flexure deformation with standard Kalman filter, and unknown flexure deformation with $H_{\infty}$ filter, respectively. Simulation results indicate that $H_{\infty}$ filter can effectively improve the accuracy of information fusion when flexure deformation is unknown but non-ignorable.

• Journal of Ocean Engineering and Technology
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• v.33 no.5
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• pp.470-478
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• 2019

Because the global positioning system (GPS) is not available in underwater environments, an inertial navigation system (INS)/doppler velocity log (DVL) integrated navigation system is generally implemented. In general, an INS/DVL integrated system adopts a loosely coupled method. However, in this loosely coupled method, although the measurement equation for the filter design is simple, the velocity of the body frame cannot be accurately measured if even one of the DVL transducer signals is not received. In contrast, even if only one or two velocities are measured by the DVL transducers, the tightly coupled method can utilize them as measurements and suppress the error increase of the INS. In this paper, a filter was designed to regenerate the measurements of failed transducers by taking advantage of the tightly coupled method. The regenerated measurements were the normal DVL transducer measurements and the estimated velocity in RPM. In order to effectively estimate the velocity in RPM, a filter was designed considering the effects of the tide. The proposed filter does not switch all of the measurements to RPM if the DVL transducer fails, but only switches information from the failed transducer. In this case, the filter has the advantage of being able to be used as a measurement while continuously estimating the RPM error state. A Monte Carlo simulation was used to determine the performance of the proposed filters, and the scope of the analysis was shown by the standard deviation ($1{\sigma}$, 68%). Finally, the performance of the proposed filter was verified by comparison with the conventional tightly coupled method.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.281-285
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• 1986

In this paper, the characteristics of disturbance torque of gimbal servo dynamics are studied, and the simulation methods of gimbal servo dynamics and INS error due to angular rate and linear acceleration of vehicle are proposed. In results of the simulation for a specific INS, it is estimated that INS velocity error due to gimbal servo dynamics is nearly proportional to square of vehicle acceleration.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.6
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• pp.481-487
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• 2013

In GPS(Global Positioning System)/INS(Inertial Navigation System) integrated navigation systems, GPS antennas and an inertial measurement units are usually installed outside and inside of the vehicle, respectively. By the difference of installed locations, performance of GPS/INS integrated navigation systems is affected by lever arm errors. For more accurate navigation, lever arm errors need to be compensated correctly since it directly affects the accuracy of navigation states. This paper proposes an effective lever arm error compensation method that utilizes velocity measurements of GPS and INS. By an experiment, feasibility of the proposed algorithm is verified. It is also shown that lever arm compensation is especially important when vehicles are experiencing rotational movements.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.1-8
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• 2004

The INS(Inertial Navigation System) provides high rate position, velocity and attitude data with good short-term stability while the GPS(Global Position System) provides position and velocity data with long-term stability. By integrating the INS with GPS, a navigation system can be achieved to Provide highly accurate navigation Performance. For the best performance, time synchronization of GPS and INS data is very important in GPS/INS integrated system But, it is impossible to synchronize them exactly due to the communication and computation time-delay. In this paper, to reduce the error caused by the measurement time-delay in GPS/INS integrated systems, error compensation methods using separate bias Kalman filter are suggested for both the loosely-coupled and the tightly-coupled GPS/INS integration systems. Linearized error models for the position and velocity matching GPS/INS integrated systems are Int derived by linearizing with respect to its time-delay and augmenting the delay-state into the conventional state equations for each case. And then separate bias Kalman Inter is introduced to estimate the time-delay during only initial navigation stage. The simulation results show that the present method is effective enough resulting in considerably less position error.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.362-368
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• 2001

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using a robust H$_2$ filter. We suggest an uncertainty model and a discrete robust H$_2$filter for INS and apply the suggested robust H$_2$ filter to the uncertainty model. The discrete robust H$_2$filter is shown by simulation to have better performance time and accuracy than Kalman filter.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.982-988
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• 2006

The errors of INS (Inertial Navigation System) are known to grow in time. To compensate the accumulated errors, measurements of external or onboard sensors are extensively utilized to form an integrated navigation system. Recently, INS/GPS integrated navigation systems have become popular since exact position and velocity information can be utilized by low cost GPS receivers. Unfortunately, this configuration cannot be trusted at all times especially when there are intentional or unexpected jammings and interruptions. To aid INS irrespectively of these cases, an INS/Image sensor integrated navigation system configuration is designed only based on the information of image sensor gimble angles. The performance of the INS/Image sensor integrated navigation system is evaluated by Monte Carlo simulation.