Extended Targets Modelling and Block Agnostic Sparse Reconstruction in Through-the-Wall Radar Imaging: A Different Perspective

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dc.contributor.authorAbdalla, Abdi T
dc.contributor.authorAlkhodary, Mohammad
dc.contributor.authorMuqaibel, Ali
dc.date.accessioned2020-01-07T13:32:54Z
dc.date.available2020-01-07T13:32:54Z
dc.date.issued2019
dc.description.abstractA common target model in through-the-wall radar (TWRI) imaging literature obeys the point target (PT) assumption in which a target is hypothesized to occupy a single pixel. Unlike PTs, the received signal reflected from extended target (ET) is an integration of the scattered signals from various parts of the same target. For high resolution images, a generalized model is needed to encompass the ETs. In this paper, we suggest a different but realistic ET reconstruction approach based on agnostic block sparsity. The algorithm does not impose any assumption on the length, number, or the distribution of the blocks. Results based on MATLAB simulation and experimental data show the effectiveness of the proposed reconstruction approach. The applications of the suggested approach are found in civil, rescue, surveillance, and security enforcement sectors, where an accurate tracking of large targets behind walls is vital.en_US
dc.description.sponsorshipThis work is funded by the National Plan for Science, Technology and Innovation (Maarifah), King Abdul Aziz City for Technology, through the Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM), The Kingdom of Saudi Arabia, award number 15-ELE4651-04en_US
dc.identifier.issn2307-1885
dc.identifier.urihttp://hdl.handle.net/20.500.11810/5349
dc.language.isoen_USen_US
dc.publisherAcademic Publication Council-Kuwait Universityen_US
dc.subjectCOMPRESSIVE SENSINGen_US
dc.subjectRADAR IMAGINGen_US
dc.subjectRADAR TARGET RECOGNITIONen_US
dc.titleExtended Targets Modelling and Block Agnostic Sparse Reconstruction in Through-the-Wall Radar Imaging: A Different Perspectiveen_US
dc.typeJournal Article, Peer Revieweden_US
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