Using neural networks to analyse surface irregularities measured with holographic radar2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)

Holographic radar is very sensitive to small irregularities in surface height [1][2]. Although this sensitivity was previously thought a disadvantage of holographic radar, recent measurements on dinosaur footprints [3] have shown that it can provide valuable information. A problem has been that the RASCAN 4 holographic radar system used in this work has provided separate signals at 5 different frequencies between say 3.6 and 4.0 GHz and at both parallel and perpendicular polarisations, each of which gives a distinct signal as a function of surface height and other variable. These signals are complicated to calculate but can be measured using a sloping surface of known height and other properties. Here neural networks are trained on a gently sloping surface of smooth sand to recognise the RASCAN signals as a function of surface height. In a testing mode, the neural networks should be able to use all the recorded signals to distinguish small differences in surface height as a function of position.