• 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 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.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.118-128
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• 2017

In this study, we propose a real time inertial navigation system/global positioning system (INS/GPS) integrated smoothing algorithm based on an extended Kalman filter (EKF) and a position damping loop (PDL) for synthetic aperture radar (SAR). Integrated navigation algorithms usually induce discontinuities due to error correction update by the Kalman filter, which are as detrimental to the performance of SAR as the relative position error. The proposed smoothing algorithm suppresses these discontinuities and also reduces the relative position error in real time. An EKF estimates the navigation errors and sensor biases, and all the errors except for the position error are corrected directly and instantly. A PDL activated during SAR operation period imposes damping effects on the position error estimates, where the estimated position error is corrected smoothly and gradually, which contributes to the real time smoothing and small relative position errors. The residual errors are re-estimated by the EKF to maintain the estimation performance and the stability of the overall loop. The performance improvements were confirmed by Monte Carlo simulations. The simulation results showed that the discontinuities were reduced by 99.8% and the relative position error by 48% compared with a conventional EKF without a smoothing loop, thereby satisfying the basic performance requirements for SAR operation. The proposed algorithm may be applicable to low cost SAR systems which use a conventional INS/GPS without changing their hardware configurations.

• Journal of Advanced Navigation Technology
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• v.11 no.2
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• pp.146-153
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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.

• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.47-53
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• 2006

In this contribution, an integration of seamless navigation system for the pedestrian is introduced. To overcome the GPS outages in various situations, multi-sensor of GPS, INS, electronic barometer and compass are considered in one Extented Kalman filter. Especially, the integrated system is designed for low-cost for the practical applications. Therefore, a MEMS IMU is considered, and the low quality of the heading is compensated by the electronic compass. In addition, only the pseudoranges from GPS measurements are considered for possible real-time application so that the degraded height is also controlled by a barometer. The mathematical models for each sensor with systematic errors such as biases, scale factors are described in detail and the results are presented in terms of a covariance analysis as well as the position and attitude errors compared to the high-grade GPS/INS combined solutions. The real application scenario of GPS outage is also investigated to assess the feasible accuracy with respect to the outage period. The description on the current status of the development and future research directions are also stated.

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

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.114.3-114
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• 2001

The pure navigation system using Inertial Navigation Unit(INU) which has very accurate short term stabilities but not long term gives rise to position errors propotional to time. On the contrary, Global Positioning System(GPS) which is bounded its errors to some fixed ranges shows higher accuracy in the long term, and lower accuracy in the short term than that of INS. Recently the integration of these two systems is one of the main topic in the field of navigation system. In this thesis, the implementation of kalman filter on the real time navigation computer and step jump error compensation method is suggested.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.135-143
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• 2009

Integrating the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor technologies using the precise GPS Carrier phase measurements is a methodology that has been widely applied in those application fields requiring accurate and reliable positioning and attitude determination; ranging from 'kinematic geodesy', to mobile mapping and imaging, to precise navigation. However, such integrated system may not fulfil the demanding performance requirements when the baseline length between reference and mobil user GPS receiver is grater than a few tens of kilometers. This is because their positioning/attitude determination is still very dependent on the errors of the GPS observations, so-called "baseline dependent errors". This limitation can be remedied by the integration of GPS and INS sensors, using multiple reference stations. Hence, in order to derive the GPS distance dependent errors, this research proposes measurement processing algorithms for multiple reference stations, such as a reference station ambiguity resolution procedure using linear combination techniques, a error estimation based on Kalman filter and a error interpolation. In addition, all the algorithms are evaluated by processing real observations and results are summarized in this paper.