Phase adjustment for multistatic passive radar imaging based on image entropy and image contrast. Issue 18 (16th September 2016)

• Record Type: Journal Article
• Authors: Wang, Jue; Wang, Jun; Wu, Yong; Zhu, Yun; Luo, Zhen; Deng, Yaqi
• Is Part Of: International journal of remote sensing. Volume 37:Issue 18(2016)
• Page Start: 4460
• Page End: 4485
• Publication Date: 2016-09-16
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1213919
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Multistatic passive radar imaging system (MPRIS) does not behave as well as traditional inverse synthetic aperture radar (ISAR) imaging system does on account of the limited illuminators of opportunity and relatively narrow bandwidth of the signals. Furthermore, in the MPRIS, limited by the unavoidable measurement errors of the positions of the illuminators or receivers, or the manoeuvrability of the moving target, the translation motion of the target cannot be compensated accurately, in which the echo will be contaminated by an error phase, resulting in the occurrence of serious image defocus and the degeneracy of the image quality. In this article, two nonparametric autofocusing techniques based on the principles of minimum entropy and maximum contrast, respectively, are proposed to deal with the undesired problems under the multistate geographical configuration consisting of one illuminator and several receivers distributed around a target. The error phase is corrected by introducing an adjustment phase in the frequency domain and the adjustment phase can be searched by iteratively solving an equation that is derived by optimizing the quality measure of the image. Since no assumption is made during the derivation, the proposed techniques could be used to compensate any form of the phase error. In addition, the influence of the multistatic configuration on the image resolution is analysed and an optimized configuration is obtained. The effectiveness of the proposed algorithms is verified via the theoretical derivations and simulations.