Adaptive Mesh in Visualization of Terrain Elevation Data

1992 ◽  
Author(s):  
J. J. Kim
Keyword(s):  
Adaptive Mesh ◽  
Elevation Data ◽  
Terrain Elevation

scholarly journals A Novel LOS Decision Technique Reflecting 3D DTED for Modeling an Electronic Warfare Environment

2021 ◽  
Vol 11 (1) ◽  
pp. 409
Author(s):  
Jaejoong Lee ◽  
Chiho Lee ◽  
Hyeon Hwi Lee ◽  
Kyung Tae Park ◽  
Hyun-Kyo Jung ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Three Dimensional ◽  
Superior Performance ◽  
Line Of Sight ◽  
Electronic Warfare ◽  
Decision Algorithm ◽  
Elevation Data ◽  
The Republic ◽  
Point Distance ◽  
Terrain Elevation

A new line-of-sight (LOS) decision algorithm applicable to simulation of electronic warfare (EW) is developed. For accurate simulation, the digital terrain elevation data (DTED) of the region to be analyzed must be reflected in the simulation, and millions of datasets are necessary in the EW environment. In order to obtain real-time results in such an environment, a technology that determines line-of-sight (LOS) quickly and accurately is very important. In this paper, a novel algorithm is introduced for determining LOS that can be applied in an EW environment with three-dimensional (3D) DTED. The proposed method shows superior performance as compared with the simplest point-to-point distance calculation method and it is also 50% faster than the conventional interpolation method. The DTED used in this paper is the data applied as level 0 for the Republic of Korea, and the decision of the LOS at approximately 1.8 million locations viewed by a reconnaissance plane flying 10 km above the ground is determined within 0.026 s.

scholarly journals Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 509
Author(s):  
Dipayan Mitra ◽  
Aranee Balachandran ◽  
Ratnasingham Tharmarasa
Keyword(s):  
Lower Bound ◽  
Estimation Error ◽  
Common Source ◽  
Bias Estimation ◽  
Tracking Accuracy ◽  
Digital Terrain ◽  
Single Target ◽  
Elevation Data ◽  
Measurement Biases ◽  
Terrain Elevation

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

scholarly journals Stochastic C-GNet Environment Modeling and Path Planning Optimization in a Narrow and Long Space

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jianjian Yang ◽  
Zhiwei Tang ◽  
Xiaolin Wang ◽  
Zirui Wang ◽  
Biaojun Yin ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Three Dimensional ◽  
Test Model ◽  
Environment Modeling ◽  
Grid Network ◽  
Planning Optimization ◽  
Elevation Data ◽  
Dynamic Stochastic ◽  
Terrain Elevation

This study proposes a novel method of optimal path planning in stochastic constraint network scenarios. We present a dynamic stochastic grid network model containing semienclosed narrow and long constraint information according to the unstructured environment of an underground or mine tunnel. This novel environment modeling (stochastic constraint grid network) computes the most likely global path in terms of a defined minimum traffic cost for a roadheader in such unstructured environments. Designing high-dimensional constraint vector and traffic cost in nodes and arcs based on two- and three-dimensional terrain elevation data in a grid network, this study considers the walking and space constraints of a roadheader to construct the network topology for the traffic cost value weights. The improved algorithm of variation self-adapting particle swarm optimization is proposed to optimize the regional path. The experimental results both in the simulation and in the actual test model settings illustrate the performance of the described approach, where a hybrid, centralized-distributed modeling method with path planning capabilities is used.

scholarly journals Analysis of the Target Detection Performance of Air-to-Air Airborne Radar Using Long-Range Propagation Simulation in Abnormal Atmospheric Conditions

2020 ◽  
Vol 10 (18) ◽  
pp. 6440
Author(s):  
Tea Heung Lim ◽  
Minho Go ◽  
Chulhun Seo ◽  
Hosung Choo
Keyword(s):  
Long Range ◽  
Target Detection ◽  
Path Loss ◽  
Detection Performance ◽  
Airborne Radar ◽  
Atmospheric Conditions ◽  
Propagation Characteristics ◽  
Digital Terrain ◽  
Elevation Data ◽  
Terrain Elevation

In this paper, we propose the analysis of the target detection performance of air-to-air airborne radars using long-range propagation simulations with a novel quad-linear refractivity model under abnormal atmospheric conditions. The radar propagation characteristics and the target detection performance are simulated using the Advanced Refractive Effects Prediction System (AREPS) software, where the refractivity along the altitude, array antenna pattern, and digital terrain elevation data are considered as inputs to obtain the path loss of the wave propagation. The quad-linear model is used to approximate the actual refractivity data, which are compared to the data derived using the conventional trilinear refractivity model. On the basis of the propagation simulations, we propose a detection performance metric in terms of the atmosphere (DPMA) for intuitively examining the long-range propagation characteristics of airborne radars in air-to-air situations. To confirm the feasibility of using the DPMA map in various duct scenarios, we employ two actual refractive indices to observe the DPMA results in relation to the height of the airborne radar.

scholarly journals Delineating flood prone areas using a statistical approach

2016 ◽  
Author(s):  
Ivan Marchesini ◽  
Mauro Rossi ◽  
Paola Salvati ◽  
Marco Donnini ◽  
Simone Sterlacchini ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Learning Algorithm ◽  
Flood Hazard ◽  
Environmental Data ◽  
Training Data ◽  
Hazard Maps ◽  
Elevation Data ◽  
Terrain Elevation ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

Floods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geo-environmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.

FRACTAL REALITY OF RANDOM DATA COMPRESSION FOR EQUAL-INTERVAL SERIES

Fractals ◽  
2000 ◽  
Vol 08 (02) ◽  
pp. 205-214 ◽  
Author(s):  
JEN-CHUNG CHEN ◽  
TZONG-YEANG LEE
Keyword(s):  
Data Compression ◽  
Fractal Interpolation ◽  
Natural Phenomena ◽  
Equal Interval ◽  
Interpolation Technique ◽  
Elevation Data ◽  
Equal Space ◽  
Terrain Elevation ◽  
First Four Moments ◽  
Space Interval

Many natural phenomena of equal-time and/or equal-space interval have been recorded. They exhibited features at many different scales of measurement — suggesting that they could be described as fractals. Two observed terrain elevation data were selected for this study. On the condition that data number was reduced properly, we used the fractal interpolation technique to reconstruct the "original" profile. The random mode was adopted for reducing the measured data. The variations of profiles compared with different ratios of data reduction and several statistics (as the first four moments) were used to test their overall properties. When data compression was as high as 70%, a resembling appearance by means of fractal reconstruction continued to exist. Its excellence appeared on the texture of profile and magnification of visualization.

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