Jonathon C. Ralston

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This paper describes the development of a new ground penetrating radar system for measuring coal thickness in underground mining operations. Although subsurface radar exhibits significant potential for depth measurement, the raw signals are complicated and cannot be readily interpreted by mining personnel. We show how real-time digital signal processing(More)
—This paper provides new solutions to the nonlinear system identification problem when the input to the system is a stationary non-Gaussian process. We propose the use of a model called the Hammerstein series, which leads to significant reductions in both the computational requirements and the mathematical tractability of the nonlinear system identification(More)
Nonlinear system identication involves selecting the order of the given model based on the input-output data. A boot-strap model selection procedure which selects the model by minimising bootstrap estimates of the prediction error is developed. Bootstrap based model selection procedures are attractive because the bootstrap observations generated for the(More)
The use of ground penetrating radar (GPR) for detecting near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe(More)
A novel pattern recognition-based approach to detect near-surface interfaces using ground penetrating radar (GPR) has been reported in [1]. The approach was used to successfully detect interfaces within 5 cm of the ground surface. This technique has been adapted for the important task of layer thickness estimation in the near-surface range. This is(More)