Kriging interpolation is largely used in geostatistics to characterise the spatial structure of data and it is established in general based upon the stationary or intrinsic assumptions; however, the consequence of this second-order approximation is that the local singularities (or extremes) could be smoothed off. This drawback could be magnified as a finerscale phenomenon is being investigated,...

A fundamental building block for supporting better utilization of radio spectrum involves predicting the impact that an emitter will have at different geographic locations. To this end, fixed sensors can be deployed to spatially sample the RF environment over an area of interest, with interpolation methods used to infer received power at locations between sensors. This paper describes a radio m...

For the six-wheel CRAB rover, this paper presents the torque control with Kriging estimation to improve the terrainability by minimizing wheel slip. In previous works, we presented a torque control approach, effectively improving terrainability by minimizing the variance of the required friction coefficient based on the static model. Using a Kalman filter to estimate the velocity information, a...

The research aim of this paper is to investigate the effectiveness of a new Kriging model which uses Taylor expansion to predict wireless network connectivity. Wireless network connectivity is measured by the strength of emitted signal power from the tower to the point in question. The prediction results are compared with those from the literature where an Ordinary Kriging model and a neural ne...

Kriging-based exploration strategies often rely on a single Ordinary Kriging model which parametric covariance kernel is selected a priori or on the basis of an initial data set. Since choosing an unadapted kernel can radically harm the results, we wish to reduce the risk of model misspecification. Here we consider the simultaneous use of multiple kernels within Kriging. We give the equations o...

This paper discusses the estimation and plug-in kriging prediction of a non-stationary spatial process assuming a smoothly varying variance function with an additive independent measurement error. A difference-based kernel smoothing estimator of the variance function and a modified likelihood estimator of the measurement error variance are used for parameter estimation. Asymptotic properties of...

Method for reducing the number of wild animal monitors is proposed by means of Kriging. Through wild animal route of simulations with 128 by 128 cells, the required number of wild animal monitors is clarified. Then it is found that the number of wild animal monitors can be reduced based on Kriging by using variograms and semi-variograms among the neighboring monitors. Also, it is found that the...

A. 2003. Comparison of krig-ing with external drift and regression-kriging. A generic framework for spatial prediction of soil variables based on regression-kriging. In further text, theory behind kriging with external drift and regression-kriging and differences between them are explained in more detail. We focus mainly on practical issues, i.e. how to derive predictions and prediction uncerta...

Environment pollution is a major problem in developing countries these days. Air pollution in particularis a cause of many diseases.In the present study an attempt has been made to map the pollution areas of Agra and predict the pollution at unsampled location.For this thirty places of Agra are selected for collectingair samples.Geographic Information System (GIS) and Kriging (Ordinary Kriging)...

In Precision Viticulture, the increasing number of information sources calls for methods to combine them and to generate information for professionals. Each data is located on its own geographical support, and geostatistics are mostly used to produce estimates on a common grid. Estimated values have to be associated with confidence intervals; using kriging variance to compute them is generally ...