Ultra Narrow Band Adaptive Tomographic Algorithm Applied to Measured Continuous Waveform Radar Data

Ultra Narrow Band Adaptive Tomographic Algorithm Applied to Measured Continuous Waveform Radar Data

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This chapter addresses the issue of spatial diversity in radar applications. Typically, information concerning ground and air targets is obtained via monostatic radar. Increased information is often equated with increased bandwidth in these radar systems. However, geometric diversity obtained through multistatic radar operations also affords the user the opportunity to obtain additional information concerning threat targets. With the appropriate signal processing, this translates directly into increased probability of detection and reduced probability of false alarm. In the extreme case, only discrete Ultra Narrow Band (UNB) frequencies of operation may be available for both commercial and military applications. With limited spectrum, UNB in the limiting case, the need for geometric diversity becomes imperative. This occurs because the electromagnetic spectrum available for commercial and military radar applications is continuously being eroded, while the need for increased information via radio frequency (RF) detection of threat targets is increasing. In addition, geometric diversity improves target position accuracy and image resolution which would otherwise remain unavailable with monostatic radar.

Inspec keywords: diversity reception; radar signal processing

Other keywords: narrow band adaptive tomographic algorithm; image resolution; continuous waveform radar data; geometric diversity; signal processing; military radar applications; commercial radar applications; multistatic radar operations; radar applications; ultra narrow band; monostatic radar; air targets; radar systems; spatial diversity; radio frequency detection; UNB frequencies

Subjects: Radar equipment, systems and applications; Signal processing and detection

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