• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.682-687
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• 2013

Navigation is a crucial capability for all types of manned or unmanned vehicles. However, vehicle navigation in underwater environments still remains a challenging problem since GPS signals for position fixes are not available in the water. Terrain-referenced underwater navigation is an alternative navigation technique that utilizes geometric information of the subsea terrain to correct drift errors due to dead-reckoning or inertial navigation. Terrain-referenced navigation requires the description of an undulating terrain surface as a mathematical function or table, which often leads to a highly nonlinear estimation problem. Recently, PFs (Particle Filters), which do not require any restrictive assumptions about the system dynamics and uncertainty distributions, have been widely used for nonlinear filtering applications. However, PF has considerable computational requirements which used to limit its applicability to problems of relatively low state dimensions. This study proposes the use of a Rao-Blackwellized particle filter that is computationally more efficient than the standard PF for terrain-referenced underwater navigation involving a moderate number of states, and its performance is compared with that of the extended Kalman filter algorithm. The validity and feasibility of the proposed algorithm is demonstrated through numerical simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.702-708
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• 2013

Underwater TRN (Underwater Terrain Referenced Navigation) estimates an underwater vehicle state by measuring a distance between the vehicle and undersea terrain, and comparing it with the known terrain database. TRN belongs to absolute navigation methods, which are used to compensate a drift error of dead reckoning measurements such as IMU (Inertial Measurement Unit) or DVL (Doppler Velocity Log). However, underwater TRN is different to other absolute methods such as USBL (Ultra-Short Baseline) and LBL (Long Baseline), because TRN is independent of the external environment. As a magnetic-field-based navigation, TRN is a kind of geophysical navigation. This paper develops an EKF (Extended Kalman Filter) formulation for underwater TRN. A filter propagation part is composed by an inertial navigation system, and a filter update is executed with echo-sounder measurement. For large-initial-error cases, an adaptive EKF approach is also presented, to keep the filter be stable. At the end, simulation studies are given to verify the performance of the proposed TRN filter. With simplified sensor and terrain database models, the simulation results show that the underwater TRN could support conventional underwater navigation methods.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.1
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• pp.85-90
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• 2013

This paper presents a study on terrain referenced navigation (TRN). The extended Kalman filter (EKF) is adopted as a filter method. A Jacobian matrix of measurement equations in the EKF consists of terrain slope terms, and accurate slope estimation is essential to keep filter stability. Two slope estimation methods are proposed in this study. Both methods are based on the least-squares approach. One is planar regression searching the best plane, in the least-squares sense, representing the terrain map over the region, determined by position error covariance. It is shown that the method could provide a more accurate solution than the previously developed linear regression approach, which uses lines rather than a plane in the least-squares measure. The other proposed method is weighted planar regression. Additional weights formed by Gaussian pdf are multiplied in the planar regression, to reflect the actual pdf of the position estimate of EKF. Monte Carlo simulations are conducted, to compare the performance between the previous and two proposed methods, by analyzing the filter properties of divergence probability and convergence speed. It is expected that one of the slope estimation methods could be implemented, after determining which of the filter properties is more significant at each mission.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.3
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• pp.307-314
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• 2017

Terrain referenced navigation(TRN) system is an attractive method for obtaining position based on terrain measurements and a terrain map. We focus on TRN systems based on the point mass filter(PMF) which is one of the recursive Bayesian method. In this paper, we propose two kinds of performance index for Bayesian filter. The proposed indices are based on entropy and mutual information from information theory. The first index measures roughness of terrain based on entropy of likelihood. The second index named by information gain, which is the mutual information between priori and posteriori distribution, is a quantity of information gained by updating measurement at each step. The proposed two indices are used to determine whether the solution from TRN is adequate for TRN/INS integration or not, and this scheme gives the performance improvement. Simulation result shows that the proposed indices are meaningful and the proposed algorithm performs better than normal TRN algorithm.

• Spatial Information Research
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• v.15 no.4
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• pp.363-370
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• 2007

This study has been carried for the development of the basic algorithm of helicopter navigation system based on TRN (Terrain Referenced Navigation) with information input from the GPS. The helicopter determines flight path due to Origination-Destination analysis on the Cartesian coordinate system of 3-D DTM. This system shows 3-D mesh map and the O-D flight path profile for the pilot's acknowledgement of the terrain, at first. The system builds TCF (terrain clearance floor) far the buffer zone upon the surface of ground relief to avid the ground collision. If the helicopter enters to the buffer zone during navigation, the real-time warning message which commands to raise the body pops up using Matlab menu. While departing or landing, control of the height of the body is possible. At present, the information (x, y, z coordinates) from the GPS is assumed to be input into the system every 92.8 m of horizontal distance while navigating along flight path. DTM of 3" interval has been adopted from that which was provided by ChumSungDae Co., Ltd..

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.161-164
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• 2010

The study on terrain referenced navigation has been proceeded from 1940s in advanced country with the object of military. In this study, the analysis regarding algorithm developed using cross-correlation matching algorithm and extended Kalman filter and simulation will be introduced. As a result, the standard deviation of position error from cross-correlation matching algorithm has been calculated 34.3m. It meant that the result has stable accuracy on the navigation. However, further study on terrain referenced navigation based on analysis of various topographic characteristics should be performed.

• 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.28 no.1
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• pp.73-82
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• 2010

TERCOM(TERrain COntour Matching), which is the one of the Terrain Referenced Navigation and used in the cruise missile navigation system, is still under development. In this study, the TERCOM based on area-based matching algorithm and extended Kalman filter is analysed through simulation. In area-based matching, the mean square difference (MSD) and cross-correlation matching algorithms are applied. The simulation supposes that the barometric altimeter, radar altimeter and SRTM DTM loaded on board. Also, it navigates along the square track for 545 seconds with the velocity of 1000km per hour. The MSD and cross-correlation matching algorithms show the standard deviation of position error of 99.6m and 34.3m, respectively. The correlation matching algorithm is appeared to be less sensitive than the MSD algorithm to the topographic undulation and the position accuracy of the both algorithms is extremely depends on the terrain. Therefore, it is necessary to develop an algorithm that is more sensitive to less terrain undulation for reliable terrain referenced navigation. Furthermore, studies on the determination of proper matching window size in long-term flight and the determination of the best terrain database resolution needed by the flight velocity and area should be conducted.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.373-382
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• 2016

In this study, an EKF (Extended Kalman Filter) based database reference navigation using both gravity gradient and terrain data was performed to complement the weakness of using only one type of geophysical DB (Database). Furthermore, a new algorithm which combines the EKF and profile matching was developed to improve the stability and accuracy of the positioning. On the basis of simulations, it was found that the overall navigation performance was improved by the combination of geophysical DBs except the two trajectories in which the divergence of TRN (Terrain Referenced Navigation) occurred. To solve the divergence problem, the profile matching algorithm using the terrain data is combined with the EKF. The results show that all trajectories generate the stable performance with positioning error ranges between 14m to 23m although not all trajectories positioning accuracy is improved. The average positioning error from the combined algorithm for all nine trajectories is about 18 m. For further study, a development of a switching geophysical DB or algorithm between the EKF and the profile matching to improve the navigation performance is suggested.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.11
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• pp.979-987
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• 2012

This paper implements and integrates algorithms for terrain following of UCAVs (Unmanned Combat Aerial Vehicles): trajectory generation, guidance, and navigation. Terrain following is very important for UCAVs because they perform very dangerous missions such as Suppression of Enemy Air Defences while the terrain following can improve the survivability of UCAVs against from the air defence systems of the enemy. To deal with the GPS jamming, terrain referenced navigation based on nonlinear filter is chosen. For the trajectory generation, Voronoi diagram is adopted to generate horizontal plane path to avoid the air defense system. Cubic spline method is used to generate vertical plane path to prevent collisions with ground while flying sufficiently close to surface. Follow-the-Carrot and pure pursuit tracking methods, which are look-ahead point based guidance algorithms, are applied for the guidance. Numerical simulation is performed to verify the performance of the integrated terrain following algorithm.