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"Data simulation"
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Raw Data Simulation of Spaceborne Synthetic Aperture Radar with Accurate Range Model
Simulated raw data have become an essential tool for testing and assessing system parameters and imaging performance due to the high cost and limited availability of real raw data from spaceborne synthetic aperture radar (SAR). However, with increasing resolution and higher orbit altitudes, existing simulation methods fail to generate SAR simulated raw data that closely resemble real raw data. This is due to approximations such as curved orbits, “stop-and-go” assumption, and Earth’s rotation, among other factors. To overcome these challenges, this paper presents an accurate range model with a “nonstop-and-go” configuration for raw data simulation based on existing time-domain simulation methods. We model the SAR echo signal and establish a precise space geometry for spaceborne SAR. Additionally, we precisely identify the target illumination area based on elliptical beams through space coordinate transformation. Finally, the SAR raw data were accurately simulated using high-precision time-domain simulation methods. The accuracy of the proposed model was validated by comparing it with the traditional hyperbolic model and the curved orbit model with “stop-and-go” assumption through image processing of the generated raw data. Through the analysis of point target quality parameters, the errors of various parameters in our distance model compared with the other two models are within 1%. Furthermore, this simulation method can be adapted to simulate raw data of other modes and satellite orbits by adjusting beam control and satellite orbit parameters, respectively. The proposed simulation method demonstrated high accuracy and versatility, thereby providing a valuable contribution to the development of remote sensing technology.
Journal Article
An enchantment of digital archaeology : raising the dead with agent-based models, archaeogaming and artificial intelligence
\"The use of computation in archaeology is a kind of magic, a way of heightening the archaeological imagination. Agent-based modelling allows archaeologists to test the 'just-so' stories they tell about the past. It requires a formalization of the story so that it can be represented as a simulation; researchers are then able to explore the unintended consequences or emergent outcomes of stories about the past. Agent-based models are one end of a spectrum that, at the opposite side, ends with video games. This volume explores this spectrum in the context of Roman archaeology, addressing the strengths, weaknesses, and opportunities of a formalized approach to computation and archaeogaming\"-- Provided by publisher.
Book
Dynamical Analyses of a Supercell Tornado in Eastern China Based on a Real-Data Simulation
Tornadoes are extremely destructive natural disasters, and East China has become a high-incidence area for tornadoes in China in recent years. On 7 July 2013, an EF2-intensity tornado occurred in Gaoyou County, Jiangsu Province in eastern China, within a supercell storm near a Meiyu frontal system. To investigate the dynamical process of the tornado, a numerical simulation was performed using four one-way nested grids within the Advanced Regional Prediction System (ARPS). Data from a nearby operational S-band Doppler radar are assimilated using a 4D ensemble Kalman filter (4DEnKF) at 5 min intervals. Forecasts are run with a nested 50 m grid, capturing the tornado embedded within the supercell storm with a reasonable agreement with observations. The tornadogenesis processes within the simulation results are analyzed in detail, including the three-dimensional evolution of the tornado vortex. It is found that a cold surge within the rear flank downdraft region plays a key role in instigating tornadogenesis when the leading edge of the cold surge approaches a near-ground convergence center located underneath the main updraft, and the enhancement of the convergence center caused by the descending of the low-level mesocyclone is the direct cause of the rapid increase in tornado vorticity. Backward trajectories are calculated based on model output, and the origins of the parcels feeding the intensifying tornado vortex are identified. It is found that parcels from the mid-level of the rear flank downdraft region follow the cold surge, descending to the ground under the influence of the downdraft in the cold surge, and then entering the convergence center, merging into the core of the tornado and being lifted up. Vertical profiles of the mass and vorticity fluxes into the core of the tornado vortex are examined, and it is found that the near-ground airflow contributes significantly to the growth of the tornado vorticity, with the contribution increasing as it gets closer to the ground.
Journal Article
Modelling of tropospheric delays in geosynchronous synthetic aperture radar
As a direct consequence of the orbital height, the integration time in geosynchronous synthetic aperture radar（GEO SAR） with metric or decimetric azimuth resolutions is in the order of several hundreds or even thousands of seconds. With such long integration time, the compensation of residual tropospheric propagation terms poses one of the fundamental challenges associated with GEO SAR missions. In order to better characterise the impact of the propagation errors on GEO SAR imaging, we put forward a model for the simulation of the tropospheric delay appropriate for the accurate simulation of GEO SAR surveys. The suggested model, with a deterministic background component and a random turbulent one, incorporates some of the most recent meteorological data for the characterization of the troposphere. To illustrate the relevance of the derivation, the suggested model is used for performance estimation and raw data simulation of GEO SAR raw data. Substantial conclusion on the system impulse response and the associated calibration requirements is also drawn from the analysis.
Journal Article
The normal law under linear restrictions: simulation and estimation via minimax tilting
Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing and is typically only feasible by using approximate Markov chain Monte Carlo sampling. We propose a minimax tilting method for exact independently and identically distributed data simulation from the truncated multivariate normal distribution. The new methodology provides both a method for simulation and an efficient estimator to hitherto intractable Gaussian integrals. We prove that the estimator has a rare vanishing relative error asymptotic property. Numerical experiments suggest that the scheme proposed is accurate in a wide range of set-ups for which competing estimation schemes fail. We give an application to exact independently and identically distributed data simulation from the Bayesian posterior of the probit regression model.
Journal Article
Business case analysis with R : simulation tutorials to support complex business decisions
This tutorial teaches you how to use the statistical programming language R to develop a business case simulation and analysis. It presents a methodology for conducting business case analysis that minimizes decision delay by focusing stakeholders on what matters most and suggests pathways for minimizing the risk in strategic and capital allocation decisions. Business case analysis, often conducted in spreadsheets, exposes decision makers to additional risks that arise just from the use of the spreadsheet environment.
Book
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments
We investigate the merits of replication, and provide methods for optimal design (including replicates), with the goal of obtaining globally accurate emulation of noisy computer simulation experiments. We first show that replication can be beneficial from both design and computational perspectives, in the context of Gaussian process surrogate modeling. We then develop a lookahead-based sequential design scheme that can determine if a new run should be at an existing input location (i.e., replicate) or at a new one (explore). When paired with a newly developed heteroscedastic Gaussian process model, our dynamic design scheme facilitates learning of signal and noise relationships which can vary throughout the input space. We show that it does so efficiently, on both computational and statistical grounds. In addition to illustrative synthetic examples, we demonstrate performance on two challenging real-data simulation experiments, from inventory management and epidemiology. Supplementary materials for the article are available online.
Journal Article