Search

The occurrence of a sudden, calamitous, and unfortunate event requires a quick response that can reduce damage and save lives, fulfilling the basic humanitarian needs of the affected population. National or international organizations and agencies may be initiated to provide assistance but local agencies need to act as soon as possible, because they are familiar with the geographical location as well as the local conditions of the affected areas. Effective coordination of disaster assistance is often crucial, particularly when resources are limited in the local agencies and the demands are more. In such conditions, it becomes a challenging job for the agencies to distribute resources so that the resources are optimally used. This article is initiated to address the challenging fact by introducing the concept of priority measure for demands. In this regard, ranking of the affected areas based on resource requirement for a disaster scenario is evaluated through a multicriteria decision-making process. A formula is developed to find the numerical value of priority factor collecting the responses of requirement of relief materials using information crowdsourcing. To this contingent, a three-echelon emergency solid transportation problem is formulated, in which the demand with the most priority is fulfilled by the local agencies in echelon-I, and the remaining demand is fulfilled by the other national or international agencies in echelon-II along with a restoration of local agencies in echelon-III. A numerical example is presented for the sake of model performance and to test the convergence using LINGO and CPLEX optimization solvers. The model is also designed using a genetic algorithm to obtain the optimal solution. Moreover, a brief analysis of the model is presented by considering 11 different numerical instances to enhance the efficacy of the model in real-life applications. [ABSTRACT FROM AUTHOR]

Over the past decade, spectrum sharing has evolved into viable commercial systems poised to meet the ever-increasing spectrum demand from service providers and end users. The key to the success of these systems is accurate and reliable propagation models that will simultaneously maximize the number of users able to access the spectrum and minimize the interference to incumbent or protected users. The classical propagation models currently utilized by these systems—the irregular terrain model (ITM) and extended hata (eHata)—do not account for features, such as foliage or endpoint clutter that can have a large impact on propagation loss. This article presents a measurement-based framework for updating these classical models and proposes a new integrated terrain and clutter model using publicly available geographic information system datasets. Over 400 000 path loss measurements were recorded in nine diverse locations across the United States at 1.7 and 3.5 GHz. Our updates improved the rms difference between measurements and model by 3–7 dB for ITM and 3–14 dB for eHata. In addition, we demonstrate several integrated terrain and clutter models that have rms differences ranging from 9.5 to 17.8 dB. Finally, cross validation was used to demonstrate the generalizability of our models to a wide variety of propagation environments. [ABSTRACT FROM AUTHOR]

This article proposes a computationally lightweight marginalized particle filtering (MPF) algorithm with improved filter consistency. For mixed linear/nonlinear state-space models such as terrain referenced navigation (TRN) models, an MPF can improve the convergence and efficiency of the filters by separately applying a Kalman filter (KF) and a particle filter (PF) to each state space rather than using only one type of filter. However, an MPF still has a high degree of complexity because it requires the same number of KFs as the number of particles in the PF. To address this issue, our method simplifies the MPF through Gaussian approximation to use only one KF and improves the filter consistency compared to other MPF simplification methods. The proposed method further reduces the complexity by considering the dynamic model of a TRN system. This article presents a complexity analysis between the original MPF and other MPF simplification methods and numerical experiments for a range/bearing example and a TRN system. The results show that the proposed algorithm has better filter consistency than the existing simplification algorithms and can achieve performance comparable to an MPF with less computation. [ABSTRACT FROM AUTHOR]

In this work, a novel single-level adaptive trajectory planner and tracking controller is developed for off-road autonomous vehicles operating on deformable terrains. Trajectory planning and tracking algorithms often rely on a simplified vehicle model to predict future vehicle states based upon control inputs, hence requiring accurate modeling and parameterization. On off-road deformable terrains this is a challenging task due to unknown terrain parameters and the complex interactions at tire-terrain interfaces, which pose issues in continuous differentiability, operating conditions, and computational time. To address these difficulties, in this paper, a neural network deformable terrain terramechanics model and its implementation within a terrain adaptive model predictive control algorithm is presented to improve vehicle safety and performance through more accurate prediction of the plant response. It is shown in simulations that the neural network is able to predict the lateral tire forces accurately and efficiently compared to the Soil Contact Model as a state-of-the-art model and is able to yield accurate bicycle model predictions. It is demonstrated that the implementation of the neural network within model predictive control can outperform both a baseline Pacejka-based and a rapidly exploring random tree controller by improving performance and allowing for more severe maneuvers to be completed that otherwise lead to failure when terrain deformations are not explicitly taken into account. The improved performance achieved through estimating terrain parameters online in an adaptive controller is highlighted against the nonadaptive realization. Finally, it is shown the algorithm is conducive to real-time implementation. [ABSTRACT FROM AUTHOR]

Proper occlusion based rendering is very important to achieve realism in all indoor and outdoor Augmented Reality (AR) applications. This paper addresses the problem of fast and accurate dynamic occlusion reasoning by real objects in the scene for large scale outdoor AR applications. Conceptually, proper occlusion reasoning requires an estimate of depth for every point in augmented scene which is technically hard to achieve for outdoor scenarios, especially in the presence of moving objects. We propose a method to detect and automatically infer the depth for real objects in the scene without explicit detailed scene modeling and depth sensing (e.g. without using sensors such as 3D-LiDAR). Specifically, we employ instance segmentation of color image data to detect real dynamic objects in the scene and use either a top-down terrain elevation model or deep learning based monocular depth estimation model to infer their metric distance from the camera for proper occlusion reasoning in real time. The realized solution is implemented in a low latency real-time framework for video-see-though AR and is directly extendable to optical-see-through AR. We minimize latency in depth reasoning and occlusion rendering by doing semantic object tracking and prediction in video frames. [ABSTRACT FROM AUTHOR]

The scattering by arbitrary inclined leaves (SAIL)-series models are some of the most well-known and widely used canopy radiative transfer models in the remote-sensing community. The latest version of 4SAIL simulates directional radiance from the optical to thermal spectrum range, but it is not suitable for sloping terrain. This limits its use in the currently ever-expanding development of applications for high-spatial-resolution observations. This study extends the 4SAIL model to 4SAILT, which considers the topographical effects on direct solar radiation and the obstruction of the surrounding topography for hemispherical radiation and the gravitropic influences on leaf angle distribution (LAD). The proposed 4SAILT model was evaluated by the 3-D discrete anisotropic radiative transfer (DART) ray-tracing model for various sky radiation conditions, soil and leaf temperatures, observational geometries, leaf area index values, and six typical LAD functions. The simulated results of directional radiance demonstrated that 4SAILT was consistent with DART, having RMSE values less than 2.0 and 0.1 W/m2/μm/sr over the 0.35–2.5 and 2.5–15 μm spectra, respectively. As an accurate, efficient, and ready-to-use model, 4SAILT benefits those who intend to use SAIL for modeling terrain areas. The 4SAILT model simulates canopy directional radiance, reflectance, emissivity, and brightness temperature over terrain surfaces, through the optical to thermal ranges. It can also be used as a surface model when estimating shortwave, longwave, and net radiation. [ABSTRACT FROM AUTHOR]

Shaded relief is an effective method for visualising terrain on topographic maps, especially when the direction of illumination is adapted locally to emphasise individual terrain features. However, digital shading algorithms are unable to fully match the expressiveness of hand-crafted masterpieces, which are created through a laborious process by highly specialised cartographers. We replicate hand-drawn relief shading using U-Net neural networks. The deep neural networks are trained with manual shaded relief images of the Swiss topographic map series and terrain models of the same area. The networks generate shaded relief that closely resemble hand-drawn shaded relief art. The networks learn essential design principles from manual relief shading such as removing unnecessary terrain details, locally adjusting the illumination direction to accentuate individual terrain features, and varying brightness to emphasise larger landforms. Neural network shadings are generated from digital elevation models in a few seconds, and a study with 18 relief shading experts found that they are of high quality. [ABSTRACT FROM AUTHOR]

Submarine topography is one of the key factors determining the course and direction of processes at the lithosphere–hydrosphere boundary. In this article, we present results of the first phase of a project to create a system for 3-D geomorphometric modeling of the Arctic Ocean floor. In this phase, a low-resolution desktop version of the system was developed. We utilized a small 5-km gridded digital elevation model extracted from the International Bathymetric Chart of the Arctic Ocean version 3.0. First, we smoothed the digital elevation model to suppress high-frequency noise. From the smoothed digital elevation model, we derived digital models of the following morphometric variables: horizontal, vertical, minimal, and maximal curvatures, catchment, and dispersive areas, as well as topographic and stream power indices. Then, to construct and visualize 3-D morphometric models of the territory, we applied an original approach for 3-D terrain modeling in the environment of Blender, free and open-source software. Finally, we present a series of the 3-D morphometric models (perspective views from the Atlantic, Eurasia, and North America). The experiment showed that the approach is efficient and can be applied for creating next desktop and web versions of the system for visualizing 3-D morphometric models of higher resolutions. The final versions of the system will be used to support marine geomorphological, geological, and biological studies of the Arctic Ocean. [ABSTRACT FROM AUTHOR]

Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitude planner ensures the robot stability by imposing limits to the angular acceleration. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. Despite the use of a simplified model, which is restricted to flat terrain, our approach shows remarkable capability to deal with a wide range of noncoplanar terrains. The results are validated by experimental trials and comparative evaluations in a series of terrains of progressively increasing complexity. [ABSTRACT FROM AUTHOR]

We implement an electromagnetic time reversal technique (EMTR) to locate lightning return strokes. The two-dimensional finite difference time domain is employed to simulate the EMTR process in both, the forward-time and the backward-time phases. Scatterers are included in the computational domain to emulate the presence of objects. Three possible criteria to find the optimum time slice of the EMTR process that includes the maximum peak field, maximum peak energy, and last local minimum of entropy are tested and it is found that only the entropy criterion can successfully locate the lightning discharge. Our analysis shows that the EMTR process in both, using an unchanged and a simplified medium for the backward time works reasonably well even with only two sensors. Furthermore, we validated the proposed method via experimental results using waveforms recorded at two sensors at distances of 14.7 and 380 km from the Säntis Tower. The results demonstrate that the EMTR back-propagation process leads to a refocusing of the radiated energy at the location of the Säntis Tower. The ambiguity in the obtained location when only two sensors are used can be resolved either by using an additional sensor or through a more accurate modeling of the terrain. [ABSTRACT FROM AUTHOR]

We present a novel solution to enable portrait relief modeling from a single image. The main challenges are geometry reconstruction, facial details recovery and depth structure preservation. Previous image-based methods are developed for portrait bas-relief modeling in 2.5D form, but not adequate for 3D-like high relief modeling with undercut features. In this paper, we propose a template-based framework to generate portrait reliefs of various forms. Our method benefits from Shape-from-Shading (SFS). Specifically, we use bi-Laplacian mesh deformation to guide the relief modeling. Given a portrait image, we first use a template face to fit the portrait. We then apply bi-Laplacian mesh deformation to align the facial features. Afterwards, SFS-based reconstruction with a few user interactions is used to optimize the face depth, and create a relief with similar appearance to the input. Both depth structures and geometric details can be well constructed in the final relief. Experiments and comparisons to other methods demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present an expectation-maximization (EM) initialization method for estimation of unknown parameters in a radar noise model. We deal with radar based terrain referenced navigation (TRN) over vegetated areas, where the radar noise was modeled as a double-mode Gaussian mixture with two unknowns. The known parameters are used for EM initialization along with innovation samples. Through TRN simulation, it is shown that the proposed EM initialization method outperforms random or k-means initialization methods in terms of both the parameter estimation error and the vehicle position error. [ABSTRACT FROM AUTHOR]

Reconstructing a 3-D scene from aerial sensor data creating a textured digital surface model (TDSM), consisting of a LiDAR point cloud and an overlaid image, is valuable in many applications including agriculture, military, surveying, and natural disaster response. When collecting LiDAR from an aircraft, the navigation system accuracy must exceed the LiDAR accuracy to properly reference returns in 3-D space. Precision navigation systems can be expensive and often require full-scale aircraft to house such systems. Synchronizing the LiDAR sensor and a camera, using a texel camera calibration, provides additional information that reduces the need for precision navigation equipment. This paper describes a bundle adjustment technique for aerial texel images that allows for relatively low-accuracy navigation systems to be used with low-cost LiDAR and camera data to form higher fidelity terrain models. The bundle adjustment objective function utilizes matching image points, measured LiDAR distances, and the texel camera calibration and does not require overlapping LiDAR scans or ground control points. The utility of this method is proven using a simulated texel camera and unmanned aerial system (UAS) flight data created from aerial photographs and elevation data. A small UAS is chosen as the target vehicle due to its relatively inexpensive hardware and operating costs, illustrating the power of this method in accurately referencing the LiDAR and camera data. In the 3-D reconstruction, the 1- $\sigma $ accuracy between LiDAR measurements across the scene is on the order of the digital camera pixel size. [ABSTRACT FROM AUTHOR]

The traditional two-way (2W) parabolic equation (PE) (2W-PE) method has been applied for modeling groundwave propagation over irregular terrain. However, based on a staircase terrain model (STM), the method is not sufficient in sloping facets modeling. Although more accurate one-way PE method based on conformal mapping model (CMM) is available, it handles only forward-propagating waves and neglects the backward-propagating waves, which become significant especially for steep terrain. In this paper, a hybrid 2W-PE method is presented to model the low-frequency (LF) groundwave propagation over irregular terrain. The method combines the STM and CMM to improve the prediction accuracy of both forward and backward propagations. The numerical results are compared with those of the conventional 2W-PE and finite-difference time-domain (FDTD) methods. While calibrated against the FDTD method, the proposed hybrid 2W-PE method has higher accuracy than the traditional 2W-PE method at the same computational cost. [ABSTRACT FROM AUTHOR]

Path loss modeling for both regular and irregular terrains is still gaining significant attention from researchers. A sloped terrain is one specific kind of an irregular terrain that—as far as we are aware—has not been completely studied. Although some results have been published for slope path loss modeling in cellular communication, an adequate model for the case when the transmitter (Tx) and the receiver (Rx) both have low-height antennas and are located on or near a slope does not exist. In this paper, for complete analysis of such conditions, we consider four scenarios: 1) two vehicles are located at opposite ends of the slope; 2) one vehicle is on the slope, and the other vehicle is beyond the slope crest; 3) one vehicle is on the slope, and the other is away from the slope at the bottom; 4) both vehicles are on the slope. For scenarios 1 and 2, we have developed analytical path loss models. We have also made some corroborating measurements for scenario 1. Scenarios 3 and 4 are addressed by models already in the literature. Simulation results for two vehicles traveling toward, on, and then beyond a slope (a sequence of our scenarios) are also shown to illustrate how sloped-terrain path loss varies in contrast to the typical flat-earth condition. Our models for scenarios 1 and 2 add to the literature and can be used to numerically estimate path losses for vehicle-to-vehicle (V2V) communication over a slope. [ABSTRACT FROM PUBLISHER]

This paper presents a feedback controller that allows MABEL, which is a kneed planar bipedal robot with 1-m-long legs, to accommodate terrain that presents large unexpected increases and decreases in height. The robot is provided no information regarding where the change in terrain height occurs and by how much. A finite-state machine is designed that manages transitions among controllers for flat-ground walking, stepping-up and -down, and a trip reflex. If the robot completes a step, the depth of a step-down or the height of a step-up can be immediately estimated at impact from the lengths of the legs and the angles of the robot’s joints. The change in height can be used to invoke a proper control response. On the other hand, if the swing leg impacts an obstacle during a step, or has a premature impact with the ground, a trip reflex is triggered on the basis of specially designed contact switches on the robot’s shins, contact switches at the end of each leg, and the current configuration of the robot. The design of each control mode and the transition conditions among them are presented. This paper concludes with experimental results of MABEL (blindly) accommodating various types of platforms, including ascent of a 12.5-cm-high platform, stepping-off an 18.5-cm-high platform, and walking over a platform with multiple ascending and descending steps. [ABSTRACT FROM PUBLISHER]

We present a method for real-time geocorrection of images from airborne pushbroom sensors using the hardware acceleration and parallel computing characteristics of modern graphics processing units. This allows very fast geocorrection without accuracy loss with respect to traditional direct methods, with very little computational load for the central processor. Our method is based on a projective texture technique originally developed for fast shadow rendering. By combining the image data with inertial navigation and positioning ancillary data, we correct the image to a digital terrain model (DTM) and produce a geocorrected and georeferenced image. The method works independently of the number of channels of the sensor. Results with an ultralight hyperspectral system show that the speed achieved with standard hardware for a 1-m grid DTM gives better than real-time performance. This allows in-flight inspection of the geocorrected image during acquisition, for more efficient coverage of large target areas. In regard to accuracy, differences with standard ray tracing direct geocorrection remained subpixel. [ABSTRACT FROM AUTHOR]

Lunar orbital imagery and laser altimeter data are two major data sources for lunar topographic modeling. Most of the previous work has processed imagery and laser altimeter data separately. Usually though, there are inconsistencies between the topographic models derived from them. This paper presents an endeavor to integrate the Chang'E-1 imagery and laser altimeter data for consistent and precision lunar topographic modeling. A combined adjustment model for the Chang'E-1 imagery and laser altimeter data is developed, in which the participants are the laser altimeter points, image exterior orientation (EO) parameters, and tie points collected from the stereo images. A weighting scheme is designed for the participants in the adjustment, and a local surface constraint is imposed to improve the adjustment performance. The output of the combined adjustment is the refined image EO parameters and laser ground points. Experimental results using the Chang'E-1 data in the Apollo 15 and 16 landing site areas show that the proposed combined adjustment approach can reduce the misregistrations between the imagery and the laser altimeter data by a maximum of 1-18 pixels in image space. The Japanese SELenological and ENgineering Explorer (SELENE) laser altimeter data at the Apollo 15 and 16 landing site areas are employed for comparison analysis. Small shifts between the SELENE and Chang'E-1 laser altimeter data were found. The topography derived from Chang'E-1 data after the combined adjustment shows a relatively consistent trend with the topography determined by the SELENE laser altimeter data. [ABSTRACT FROM AUTHOR]

The Earth's topography, including vegetation and human-made features, reduced to a virtual 3-D representation is a key geographic layer for any extended development or risk management project. Processed from multiple aerial images or from airborne lidar systems, the 3-D topography is first represented as a point cloud. This paper deals with the generation of digital terrain models (DTMs) in natural landscapes.We present a global methodology for estimating the terrain height by deriving a predictive filter paradigm. Under the assumption that the terrain topography (elevation and slope) is regular in a neighboring system, a predictive filter combines linearly the predicted topographic values and the effectivemeasured values. In this paper, such a filter is applied to 3-D lidar data which are known to be of high elevation accuracy. The algorithm generates an adaptive local geometry wherein the elevation distribution of the point cloud is analyzed. Since local terrain elevations depend on the local slope, a predictive filter is first applied on the slopes and then on the terrain elevations. The algorithm propagates through the point cloud following specific rules in order to optimize the probability of computing areas containing terrain points. Considered as an initial surface, the previous DTM is finally regularized in a Bayesian framework. Our approach is based on the definition of an energy function that manages the evolution of a terrain surface. The energy is designed as a compromise between a data attraction term and a regularization term. The minimum of this energy corresponds to the final terrain surface. The methodology is discussed, and some conclusive results are presented on vegetated mountainous areas. [ABSTRACT FROM AUTHOR]

The article presents a statistical method for predicting rover mobility that incorporates terrain uncertainty. It notes that the said method considers the uncertainties of the physical parameters of the terrain through stochastic response surface method (SRSM). SRSM is one of the two techniques which predict the efficiency of the mobility of mobile robots. Likewise, the statistical method used the Monte Carlo method which requires a large number of analytical and numerical simulation trials.

This paper provides a methodology for the estimation of resistance, thrust, and resistive torques on each wheel of a rigid-wheeled vehicle generated at the vehicle-terrain interface, and from these forces and moments, a methodology to estimate terrain parameters is presented. Terrain force estimation, which is independent of a terrain model, can infer the ability to accelerate, climb, or tow a load independent of the underlying terrain properties. When a terrain model is available, parameters of that model, such as soil cohesion, friction angle, maximum normal stress, and stress distribution parameters, are determined from estimated vehicle-terrain forces using a multiple-model estimation approach, providing parameters that relate to accepted mobility metrics. The methodology requires a standard proprioceptive sensor suite-accelerometers, rate gyros, wheel speeds, motor torques, and ground speed. Sinkage sensors are not required. Simulation results demonstrate efficacy of the method on three terrains spanning a range of soil cohesions reported in the literature. [ABSTRACT FROM AUTHOR]

In space-time adaptive processing (STAP), the clutter covariance matrix is routinely estimated from secondary "target-free" data. Because this type of data is, more often than not, rather scarce, the so-obtained estimates of the clutter covariance matrix are typically rather poor. In knowledge-aided (KA) STAP, an a priori guess of the clutter covariance matrix (e.g., derived from knowledge of the terrain probed by the radar) is available. In this note, we describe a computationally simple and fully automatic method for combining this prior guess with secondary data to obtain a theoretically optimal (in the mean-squared error sense) estimate of the clutter covariance matrix. The authors apply the proposed method to the KASSPER data set to illustrate the type of achievable performance. [ABSTRACT FROM AUTHOR]

In this paper, we present an example-based system for terrain synthesis. In our approach, patches from e sample terrain (represented by a height field) are used to generate a new terrain. The synthesis is guided by a user-sketched feature map that specifies where terrain features occur in the resulting synthetic terrain. Our system emphasizes large-scale curvilinear features (ridges and valleys) because such features are the dominant visual elements in most terrains. Both the example height field and user's sketch map are analyzed using a technique from the field of geomorphology. The system finds patches from the example data that match the features found in the user's sketch. Patches are joined together using graph cuts and Poisson editing. The order in which patches are placed in the synthesized terrain is determined by breadth-first traversal of a feature tree and this generates improved results over standard raster-scan placement orders. Our technique supports user-controlled terrain synthesis in a wide variety of styles, based upon the visual richness of real-world terrain data. [ABSTRACT FROM AUTHOR]

The case of multiple diffraction from a cascade of two dimensional multi-shaped canonical objects such as wedges, knife-edges and cylinders, is investigated. A heuristic slope uniform theory of diffraction (UTD) solution is presented that can give accurate and uniform field predictions at any area point and for any kind of environment. The solution is then compared against measurements taken in an anechoic chamber and a very good agreement is observed. Furthermore, an algorithm for implementing the proposed UTD solution to rural area field predictions is also presented, based on a novel algorithm for optimum fit of canonical shapes to the terrain irregularities. Comparisons between the model and measurements taken in real terrain profile show a very good agreement and uniform field predictions. The importance of the choice of the used canonical object to terrain modeling is also discussed since different diffraction patterns are observed for different combination of the canonical objects. [ABSTRACT FROM AUTHOR]

Topographic correction based on sun-canopy-sensor (SCS) geometry is more appropriate than terrain-based corrections in forested areas since SCS preserves the geotropic nature of trees (vertical growth) regardless of terrain, view, and illumination angles. However, in some terrain orientations, SCS experiences an overcorrection problem similar to other simple photometric functions. To address this problem, we propose a new SCS+C correction that accounts for diffuse atmospheric irradiance based on the C-correction. A rigorous, comprehensive, and flexible method for independent validation based on canopy geometric optical reflectance models is also introduced as an improvement over previous validation approaches, and forms a secondary contribution of this paper. Results for a full range of slopes, aspects, and crown closures showed SCS+C provided improved corrections compared to the SCS and four other photometric approaches (cosine, C, Minnaert, statistical-empirical) for a Rocky Mountain forest setting in western Canada. It was concluded that SCS+C should be considered for topographic correction of remote sensing imagery in forested terrain. [ABSTRACT FROM AUTHOR]

We develop a method for the loading of ground by moving vehicles in large finite-difference time-domain simulations of seismic wave propagation. The objective is to realistically produce two distinct types of ground loading for either wheeled or tracked vehicles in our propagation models: lower frequency loading associated with suspension dynamics and higher frequency impulsive loading associated with tire treads or wheels rolling over individual track blocks. These loading characteristics are important because field measurements show that vehicle ground forcing in both frequency bands produces seismic surface waves that networked sensors can remotely process for security applications. The method utilizes a vehicle-dynamics model to calculate a response to vehicle acceleration and ground features such as bumps; calculates forces transmitted to the ground; distributes these forces to staggered points of a finite-difference model; and simulates seismic wave propagation away from the vehicle. We demonstrate the method using bounce-and-pitch models of wheeled and tracked vehicles. We show that by carefully preprocessing force inputs, we can accurately simulate wave propagation and seismic signatures in finite-difference analyses of vehicles moving continuously over terrain. [ABSTRACT FROM AUTHOR]

A novel technique for the retrieval of a digital terrain model (DTM) with a ground-based synthetic aperture radar (GB-SAR) system is described. A set of SAR images collected over a test site located at Salice d'Ulzio, Italy, is used to validate experimentally the proposed DTM retrieval technique. The topographic height is obtained using an algorithm specifically tailored for a GB-SAR system equipped with two receive antennas. The comparison with an existing DTM of the test site area shows that the retrieved DTM is accurate, with root mean square height differences between the two DTMs below 5 m. [ABSTRACT FROM AUTHOR]

A new state-equation of the induction motor that is useful for transparent design of the vector control system is presented. In vector control, high performance of the current control is realized by the decoupling control. In case of the conventional state-equation and block diagram, the physical operation of the induction motor is difficult to understand from the block diagram. This is because, in the conventional block diagram, the dimension of the summing point has no physical meaning. To over- come this problem, a new state-equation and block diagram of induction motor will be proposed. In this proposed block diagram, all branches of the summing point have voltage dimension, and also the existence of couplings is clarified by matrix method. Consequently, the proposed equation enables easy understanding of the physical operation of the machine, and design of the decoupled controller become transparent and easy. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging is emerging as a tool to provide accurate digital terrain models (DTMs) of forest areas, since it can penetrate beneath the canopy. Although traditional techniques, such as linear prediction, have shown to be robust type methods for the extraction of DTMs, they fail to effectively model terrain with steep slopes and large variability. In this paper, a modified linear prediction techniques, followed by adaptive processing and refinement, is developed. A comparison with the traditional linear prediction method is provided along with statistical measures to ascertain the validity of the foregoing technique. [ABSTRACT FROM AUTHOR]

Examines the errors encountered when estimating vegetation canopy parameters from reflectance measured over small slopes, under the common assumption that minor variations in topography can be neglected. Common example of where modeling and measurement conditions may be inconsistent; Definition of measurement and modeling error; Effect of topography on reflectance.

Presents information on a study on the error models of slope and aspect of a terrain derived from data extracted from a geographic information system. Assessment of error; Distribution of errors in slope and aspect; Evaluation of the error models; Discussion and conclusions.

Describes an integral equation (IE) solution to ultrahigh frequency (UHF) terrain path-loss computation. Requirements for wireless communication systems planning; Adoption of a two-dimensional terrain model; Features of the terrain model.