Pseudo‐Zernike‐based multi‐pass automatic target recognition from multi‐channel synthetic aperture radar. Issue 4 (1st April 2015)

• Record Type: Journal Article
• Authors: Clemente, Carmine; Pallotta, Luca; Proudler, Ian; De Maio, Antonio; Soraghan, John J.; Farina, Alfonso
• Is Part Of: IET radar, sonar & navigation. Volume 9:Issue 4(2015)
• Page Start: 457
• Page End: 466
• Publication Date: 2015-04-01
• Subjects: military radar -- synthetic aperture radar -- radar imaging -- feature extraction -- object recognition -- Zernike polynomials; pseudoZernike‐based multipass automatic target recognition -- multichannel synthetic aperture radar platform -- battlefield scenario -- uncertainty mitigation -- ATR -- spatial diversity -- channel diversity -- feature extraction -- target identification -- pZm invariant properties -- limited computational complexity -- data transfer requirements -- Gotcha dataset; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2014.0296
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provides the opportunity to exploit diversities to mitigate uncertainty. In this study, the authors address the problem of automatic target recognition (ATR) from synthetic aperture radar platforms. The author's approach exploits both channel (e.g. polarisation) and spatial diversity to obtain suitable information for such a critical task. In particular they use the pseudo‐Zernike moments (pZm) to extract features representing commercial vehicles to perform target identification. The proposed approach exploits diversities and invariant properties of pZm leading to high confidence ATR, with limited computational complexity and data transfer requirements. The effectiveness of the proposed method is demonstrated using real data from the Gotcha dataset, in different operational configurations and data source availability.