Open AccessA subspace‐based channel calibration algorithm for geosynchronous satellite‐airborne bistatic multi‐channel radars
Author(s) -
Zhang Dandan,
Qiu Xiaolan,
Hu Donghui,
Lv Xiaolei,
Huang Lijia,
Ding Chibiao
Publication year - 2014
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2013.0325
Subject(s) - remote sensing , geosynchronous orbit , channel (broadcasting) , satellite , subspace topology , bistatic radar , calibration , computer science , algorithm , radar , geology , telecommunications , mathematics , engineering , aerospace engineering , artificial intelligence , radar imaging , statistics
Ground moving target indication based on geosynchronous (GEO) satellite‐airborne (SA) bistatic radar can keep a certain area under continuous surveillance, and thus has important military application values. However, a channel error will affect the clutter suppression performance of the bistatic multi‐channel moving target indication radar. And it is found that the channel calibration algorithm used in the long sequence monostatic side‐looking synthetic aperture radar, is not applicable to the long sequence translational variant bistatic radar. An improved subspace‐based channel calibration algorithm is proposed in this study to deal with channel errors of the GEO SA bistatic multi‐channel radar. The algorithm utilises the clutter as calibrated sources, and estimates channel error parameters by minimising the projections of the calibrated sources onto the noise subspace. As the channel errors are estimated from the data, the proposed algorithm does not need any extra calibration sources. Also, the proposed algorithm does not have the limitation of the traditional array self‐calibration algorithms, which may have non‐unique estimations under the condition of linear arrays. Besides, it avoids finding the inversion of large‐size matrix and calculating with any iteration, and hence it is efficient. Moreover, the proposed algorithm can estimate channel errors that vary with azimuth. Simulations are performed to validate the proposed algorithm.