Dynamic aircraft identification using HRRP under attitude perturbation interference

Jia Liu and Bao Fa Wang

Journal of Electromagnetic Waves and Applications, 2019, vol. 33, issue 7, 929-945

Abstract: The Automatic Target Recognition (ATR) with target High-Resolution Range Profile (HRRP) is of great research and application significance. Environmental interferences reflect close relevance with target HRRP imaging quality as well as classification performance. The dynamic target attitude perturbation is considered as one of the dominant interference factors. This paper studies its impact on aircraft HRRP through simulation and statistical analysis. HRRP confidence zone and similarity concepts are proposed as qualitative and quantitative tools for HRRP variation description under attitude perturbation. Extracted HRRP variation features provide a novel solution to overcome attitude perturbation interference in target classification while maintaining the accuracy. Classification evaluations over three aircraft models in accuracy and efficiency consolidate the proposed classification method which better suits the practical classification scenario.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/09205071.2018.1555493 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tewaxx:v:33:y:2019:i:7:p:929-945

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tewa20

DOI: 10.1080/09205071.2018.1555493

Access Statistics for this article

Journal of Electromagnetic Waves and Applications is currently edited by Mohamad Abou El-Nasr and Pankaj Kumar Choudhury

More articles in Journal of Electromagnetic Waves and Applications from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().