Application of Time-Difference-of-Arrival Localization Method in Impulse System Radar and the Prospect of Application of Impulse System Radar in the Internet of Things
The impulse system radar is based on the generation, transmission, radiation, receiving, data collection, data processing, and imaging of impulse signals. It identifies the position or shape of the target object by accurately measuring the time difference and the waveform parameter difference of the...
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Veröffentlicht in: | IEEE access 2018-01, Vol.6, p.44846-44857 |
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Sprache: | eng |
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Zusammenfassung: | The impulse system radar is based on the generation, transmission, radiation, receiving, data collection, data processing, and imaging of impulse signals. It identifies the position or shape of the target object by accurately measuring the time difference and the waveform parameter difference of the electromagnetic signals reflected by the target object. It is a typical impulse-system-based ultra-wideband radar. Based on the existing studies on transmission characteristics of impulse signals and radiation characteristics of elementary time-domain antennas, a "point source approximation" analytical model, which considers an elementary antenna as the radiation point source and can reflect the radiation characteristics of specific elementary antennas, is proposed. On this basis, a time-difference-of-arrival (TDOA) method, whereby targets are localized by testing the TDOA of non-collinear multi-antenna (three or more) for target echo signaling, is proposed. Meanwhile, a detailed analytical analysis and numerical calculation programming are performed. Experimental test results show that the present method enables fairly accurate localization of targets. The application of time-difference localization in an impulse radar can also be extended to the Internet of Things. Positioning and tracking targets by an impulse radar is exactly a key technology in the application of Internet of Things. The research work done in this paper has excellent application values in Internet of Things. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2018.2864611 |