Orthogonal frequency division multiplexing phenomenology: recognition of canonical scatterers using flat spectra OFDM pulses. Issue 4 (1st April 2016)

• Record Type: Journal Article
• Authors: Lellouch, Gabriel; Mishra, Amit Kumar; Inggs, Michael
• Is Part Of: IET radar, sonar & navigation. Volume 10:Issue 4(2016)
• Page Start: 647
• Page End: 654
• Publication Date: 2016-04-01
• Subjects: radar signal processing -- OFDM modulation -- radar target recognition -- electromagnetic wave scattering -- intersymbol interference -- echo suppression -- probability -- signal classification -- AWGN; flat spectrum orthogonal frequency division multiplexing pulse -- canonical scatterer recognition -- flat spectra OFDM pulse -- radar return -- diffraction geometrical theory -- scattering model -- intersymbol interference mitigate -- target scatterer -- received echo spectrum -- spectrum slope estimation -- classification probability -- integration technique -- low signal‐to‐noise ratio condition -- classification rate improvement; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2014.0469
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : In this study, the authors present a novel approach to characterise scattering centres from their radar return using specially designed waveforms. The widely accepted scattering model derived from the geometrical theory of diffraction is considered. They show how the use of flat spectrum orthogonal frequency division multiplexing pulses can help discriminating between the various scattering mechanisms. The technique, which assumes prior information about the target runs into two steps. The first step consists of designing pulses of appropriate length to mitigate intersymbol interference when several scatterers compose the target. In the second step, they calculate the spectrum of the received echo and estimate the slope of this spectrum, whose value is used to infer the scatterer type. Eventually, they assess the sensitivity of this approach with respect to additive white Gaussian noise in terms of the overall probability of correct classification and show that an integration technique can help improve the classification rate even in low signal‐to‐noise ratio conditions.