Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting

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


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.


External organisations
  • University of Electronic Science and Technology of China
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Signal Processing
Original languageEnglish
Title of host publicationIGARSS 2019
Subtitle of host publication2019 IEEE International Geoscience and Remote Sensing Symposium
Place of PublicationYokohama, Japan
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)978-1-5386-9154-0
ISBN (Print)978-1-5386-9155-7
Publication statusPublished - 2019 Nov 14
Publication categoryResearch
EventIGARSS 2019 : 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan
Duration: 2019 Jul 282019 Aug 2


ConferenceIGARSS 2019