Capineri, Lorenzo

Windsor, C. G.

English

Florence Research

954 Progress In Electromagnetics Research Symposium Abstracts, Marrakesh, Morocco, Mar. 20–23, 2011Distinguishing Buried Mines from Battlefield Clutter UsingHolographic RadarC. G. Windsor1 and L. Capineri21United Kingdom Atomic Energy Authority (UKAEA), 116 New Road, East Hagbourne, OX11 9LD, UK2Dipartimento Elettronica e Telecomunicazioni, Universita` di Firenze, Firenze, ItalyAbstract— It is said that 90% of a deminer’s time is spent locating and removing harmlessclutter objects. In order to address whether this situation can be improved, a series of holographicradar scans have been made using the Rascan holographic radar system of a simulated mine(a cylindrical plastic sweet container filled with sugar) and several common battlefield clutterobjects: a short length of barbed wire, a small unexploded bullet, a coke tin and a large flintstone.The raw Rascan images which consist of 5 frequencies at parallel and cross polarisations have beencombined together by taking the modulus of the image amplitude less the mean background andsumming over all the frequencies and two polarisations. The series consists of the same objectsplaced on a 400mm × 470mm area measured at10mm resolution in a sand box filled with finedry sand at about 60mm depth. Some 15 similar images were taken with the same objects placedat different positions over the area and at different depths and orientations.The above figure shows that the eye, armed with the knowledge of what the Rascan imagesrepresent, is readily able to identify them from a combination of their shape, their amplitude,and their “texture” or degree of un-evenness of amplitude. The question is, “Can the skills ofthe human eye be reproduced by an automated system?”.The image is scanned by a receptive field of a size large enough to contain the objects to bediscriminated. If the amplitude above background over the receptive field exceeds some definedthreshold, the position of maximum integrated amplitude is located. The part of the imagewithin the receptive field placed optimally over the located object is saved by the system, as anexample representation of that object, to be used in subsequent training. The coloured boxes inthe figure show such automatically generated object images.The series of “training” images of known objects is presented to a neural network which is ableto adjust its complexity so that other “test” images are optimally classified. Finally quite new“validation” images which have not been previously presented to the network can be used toidentify the classification accuracy in terms of the percentage of correctly classified objects andthe percentage of false alarms.(a) (b)Figure 1: (a) The Rascan composite image and (b) a photograph of the corresponding objects before beingburied under the sand. The coloured boxes show the areas of the image picked out by a scanned receptivefield, which can be used for training a neural network.

Distinguishing Buried Mines from Battlefield Clutter UsingHolographic Radar

https://flore.unifi.it/bitstream/2158/667314/1/Capineri_Windsor_PIERS2011.pdf

Distinguishing Buried Mines from Battlefield Clutter UsingHolographic Radar

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