• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.131-139
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• 2013

In recent years alternative navigation system such as a DBRN (Data-Base Referenced Navigation) system using geophysical information is getting attention in the military navigation systems in advanced countries. Specifically TRN (Terrain Referenced Navigation) algorithm research is important because TRN system is a practical DBRN application in South Korea at present time. This paper presents an integrated navigation algorithm that combines a linear profile-based TRN and INS (Inertial Navigation System). We propose a correlation analysis method between TRN performance and terrain roughness index. Then we propose a conditional position update scheme that utilizes the position output of the conventional linear profile type TRN depending on the terrain roughness index. Performance of the proposed algorithm is verified through Monte Carlo computer simulations using the actual terrain database. The results show that the TRN/INS integrated algorithm, even when the initial INS error is present, overcomes the shortcomings of linear profile-based TRN and improves navigation performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.367-377
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• 2019

As an alternative navigation system for the non-GNSS (Global Navigation Satellite System) environment, a new type of DBRN (DataBase Referenced Navigation) which applies both gravity gradient and terrain, and combines filter-based algorithm with profile matching was suggested. To improve the stability of the performance compared to the previous study, both centralized and decentralized EKF (Extended Kalman Filter) were constructed based on gravity gradient and terrain data, and one of filters was selected in a timely manner. Then, the final position of a moving vehicle was determined by combining a position from the filter with the one from a profile matching. In the simulation test, it was found that the overall performance was improved to the 19.957m by combining centralized and decentralized EKF compared to the centralized EKF that of 20.779m. Especially, the divergence of centralized EKF in two trajectories located in the plain area disappeared. In addition, the average horizontal error decreased to the 16.704m by re-determining the final position using both filter-based and profile matching solutions. Of course, not all trajectories generated improved performance but there is not a large difference in terms of their horizontal errors. Among nine trajectories, eights show smaller than 20m and only one has 21.654m error. Thus, it would be concluded that the endemic problem of performance inconsistency in the single geophysical DB or algorithm-based DBRN was resolved because the combination of geophysical data and algorithms determined the position with a consistent level of error.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.63-73
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• 2014

Recently, researches on DBRN(DataBase Referenced Navigation) system are being carried out to replace GNSS(Global Navigation Satellite System), as weaknesses of GNSS were found that are caused by the intentional interference and the jamming of the satellite signal. This paper describes the gravity gradient modeling and the construction of EKF(Extended Kalman Filter) based GGRN(Gravity Gradient Referenced Navigation). To analyze the performance of GGRN, fourteen flight trajectories were made for simulations over whole South Korea. During the simulations, we considered the errors in both DB(DataBase) and sensor as well as the flight altitudes. Accurate performances were found, when errors in the DB and the sensor are small and they located at lower altitude. For comparative evaluation, the traditional TRN(Terrain Referenced Navigation) was also developed and performances were analyzed relative to those from the GGRN. In fact, most of GGRN performed better in low altitude, but both of precise gravity gradient DB and gradiometer were required to obtain similar level of precisions at the high altitude. In the future, additional tests and evaluations on the GGRN need to be performed to investigate on more factors such as DB resolution, flight speed, and the update rate.