• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.7
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• pp.629-639
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• 2016

Batch process TRN(Terrain Relative Navigation) using an altimeter is a technique to correct position by correlating a series of periodically measured terrain height profile and terrain height candidate profile of the DEM(Digital Elevation Map). However, it is generally known that the performance of TRN is degraded when measured terrain height profile and terrain height candidate profiles of the DEM are similar at hill or repetitive terrain. In this paper, area based terrain slope roughness index[11] is applied and area based terrain curvature roughness index which can detect similarity of terrain in ROI(Region Of Interest) is proposed to overcome this problem. Applying terrain roughness indexes to terrain relative navigation system of lunar lander, it is shown that TRN using area based terrain roughness results in improved performance compared to conventional trajectory based method through simulation.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1015-1023
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• 2022

The Entry-Descent-Landing process of a lander involves many environmental and technical challenges. To solve these problems, recently, terrestrial relative navigation (TRN) technology has been essential for landers. TRN is a technology for estimating the position and attitude of a lander by comparing Inertial Measurement Unit (IMU) data and image data collected from a descending lander with pre-built reference data. In this paper, we present a method for generating descent dataset and extracting landmarks, which are key elements for developing TRN technologies to be used on Mars. The proposed method generates IMU data of a descending lander using a simulated Mars landing trajectory and generates descent images from high-resolution ortho-map and digital elevation map through a ray tracing technique. Landmark extraction is performed by an area-based extraction method due to the low-textured surfaces on Mars. In addition, search area reduction is carried out to improve matching accuracy and speed. The performance evaluation result for the descent dataset generation method showed that the proposed method can generate images that satisfy the imaging geometry. The performance evaluation result for the landmark extraction method showed that the proposed method ensures several meters of positioning accuracy while ensuring processing speed as fast as the feature-based methods.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.734-745
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• 2017

The navigation system of lunar lander are composed of various navigation sensors which have a complementary characteristics such as inertial measurement unit, star tracker, altimeter, velocimeter, and camera for terrain relative navigation to achieve the precision and autonomous navigation capability. The required performance of sensors has to be determined according to the landing scenario and mission requirement. In this paper, the specifications of navigation sensors are investigated through covariance analysis. The reference error model with 77 state vector and measurement model are derived for covariance analysis. The mission requirement is categorized as precision exploration with 90m($3{\sigma}$ ) landing accuracy and area exploration with 6km($3{\sigma}$ ), and the landing scenario is divided into PDI(Powered descent initiation) and DOI(Deorbit initiation) scenario according to the beginning of autonomous navigation. The required specifications of the navigation sensors are derived by analyzing the performance according to the sensor combination and landing scenario.

• 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.