Abdulhakim Daluom, M. Wicks, H. Abdelbagi, Abdunaser Abdusamad, Muftah Akroush, Turki M. Alanazi
{"title":"Optimal sensor geometry for tomographic below ground imaging of objects in a region of interest","authors":"Abdulhakim Daluom, M. Wicks, H. Abdelbagi, Abdunaser Abdusamad, Muftah Akroush, Turki M. Alanazi","doi":"10.1109/NAECON.2017.8268788","DOIUrl":null,"url":null,"abstract":"In this paper, we present a unique Ground Penetrating Radar (GPR) technique currently under development to image deeply buried targets over relatively small areas of regard. This GPR computes the optimal sensor geometry for a given bistatic and multi-static radar distribution of transmitters and receivers operating in the microwave band of frequencies. There are M receivers, and they can be arranged arbitrarily. In addition to that, we have N transmitters that we can also arrange in the same way. Each receiver can process all frequencies from the multiple transmitters. The transmitters are deployed above ground, while the receivers are in the space between the surface of the ground and the multitude of transmitters. Radio frequency (RF) tomography is developed using Green's functions and Maxwell's equations to develop an algorithm for imaging underground targets. The targets are assumed to be inside a region of interest (ROI). The design includes different transmitter (Tx) and receiver (Rx) geometries, such as concentric circles and concentric squares. Based on these different geometries we determine which distribution is best for imaging depths of shallow buried targets. Also, the development of the optimal sensor geometry is investigated in order to provide the best configurations and increase overall tomographic imagery. Simulation results show excellent performance in the absence of significant unknown disturbances.","PeriodicalId":306091,"journal":{"name":"2017 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE National Aerospace and Electronics Conference (NAECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON.2017.8268788","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we present a unique Ground Penetrating Radar (GPR) technique currently under development to image deeply buried targets over relatively small areas of regard. This GPR computes the optimal sensor geometry for a given bistatic and multi-static radar distribution of transmitters and receivers operating in the microwave band of frequencies. There are M receivers, and they can be arranged arbitrarily. In addition to that, we have N transmitters that we can also arrange in the same way. Each receiver can process all frequencies from the multiple transmitters. The transmitters are deployed above ground, while the receivers are in the space between the surface of the ground and the multitude of transmitters. Radio frequency (RF) tomography is developed using Green's functions and Maxwell's equations to develop an algorithm for imaging underground targets. The targets are assumed to be inside a region of interest (ROI). The design includes different transmitter (Tx) and receiver (Rx) geometries, such as concentric circles and concentric squares. Based on these different geometries we determine which distribution is best for imaging depths of shallow buried targets. Also, the development of the optimal sensor geometry is investigated in order to provide the best configurations and increase overall tomographic imagery. Simulation results show excellent performance in the absence of significant unknown disturbances.