DSDS 19

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https://dspace.lib.cranfield.ac.uk/handle/1826/21164

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Browsing DSDS 19 by Subject "'Bistatic'"

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    Buried object detection and classification using SAR in varying moisture environments
    (Cranfield University, 2020-01-08 10:18) Sabiers, Richard
    Remote investigation and classification of buried objects is important for many reasons. For defence roles the identification of suspicious objects can enhance security. Civilian applications are also applicable allowing estimation of crop yield and remote monitoring of plant health. The aim of this work is to demonstrate a prototype open-source system of radar-based target detection and classification. Experimental targets consist of buried artefacts, including an example of military ordnance, such as a landmine and a metallic improvised explosive device. These represent real world examples, chosen due to differing composition of materials, and will be imaged when buried in a medium of top soil with different moisture levels.Detection of targets is a three-stage pipeline of data collection, image formation and classification. Data is collected using Cranfield University’s prototype mini-GBSAR system, which is deployable in field. This tool enables sub-surface sensing by measuring the backscatter of electromagnetic waves in the microwave region. Synthetic aperture radar signal processing is used to produce the final three-dimensional image. Image formation is beneficial for an intelligent machine classifier to evaluate the data and to separate objects as targets of interest from irrelevant clutter.
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    ItemOpen Access
    Three-Dimensional Radar image overlaid on a photograph
    (Cranfield University, 2020-01-09 09:24) Sabiers, Richard
    This picture is important because it allows you to visually gain an understanding of how an object may be interpreted by radar. A single high-resolution photographic image taken of an object and the resulting 3D radar image generated from the model. A synthetic aperture radar (SAR) volumetric image of a target was formed with Cranfield’s mini-GBSAR scanner, then overlaid over the target’s photograph. This shows a visual representation of backscattered electromagnetic waves. By comparing the representation against the original, a better understanding of the radar features is attained. This relates to my work on understanding radar object characteristics for classification using machine learning techniques.