Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Bibtex

@inproceedings{b4b95192cd474d04a5f133a6ca2a157c,
title = "Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting",
abstract = "Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods.",
author = "Yongchao Zhang and Deqing Mao and Yuanyuan Bu and Junjie Wu and Yulin Huang and Andreas Jakobsson",
year = "2019",
month = "11",
day = "14",
doi = "10.1109/IGARSS.2019.8899156",
language = "English",
isbn = "978-1-5386-9155-7",
pages = "8562--8565",
booktitle = "IGARSS 2019",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

}