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Telecommunications and Radio Engineering
SJR: 0.202 SNIP: 0.2 CiteScore™: 0.23

ISSN Imprimer: 0040-2508
ISSN En ligne: 1943-6009

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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v75.i4.40
pages 333-353


J. T. Astola
Tampere University of Technology, Signal Processing Laboratory, P. O. Box 553, FIN-33101, Tampere, Finland
Karen O. Egiazarian
Tampere University, Tampere, 33720, Finland
A. V. Totsky
National Aerospace University (Kharkiv Aviation Institute), 17, Chkalov St., Kharkiv, 61070, Ukraine
A. A. Zelensky
A. Usikov Institute of Radio Physics and Electronics, National Academy of Sciences of Ukraine, 12, Academician Proskura St., Kharkov; and National Aerospace University (Kharkov Aviation Institute), 17, Chkalov St.., Kharkov, Ukraine


In this paper, application of bispectrum-based signal processing to problem of radar range profile (RRP) reconstruction is considered for the purposes of naval object detection and recognition in sea clutter. It is demonstrated by the analysis of the received radar backscattered signal, that it contains several temporal and spatial non-stationary contributions caused by sea clutter. Two different techniques used for estimation of the RRPs are considered and their performances are studied experimentally. The first considered technique exploits the averaging of the after-detection envelopes performed for the sequence of accumulated radar pulse echo-signals. The second technique is based on the averaging of the sequence of bispectral estimates computed for the sequence of the radar echo-signals and following reconstruction of the backscattered signal envelope. State of the art for experimental measurements carried out by X-band radar in real-life sea clutter environment is described. Analysis of the experimental results obtained for real radar data is performed for different polarization combinations and various sea clutter conditions caused by wind speed. The plots of the RRPs are demonstrated for different cases of sea state. The benefits of the bispectrum-based technique as compared with common ensemble averaging technique are illustrated.