For some applications, data burden can become a problem if the system needs to resolve targets accurately. Passive coherent location using digital video broadcast signals is not an exception: large amounts of data need to be acquired, transferred, and stored for processing. Redundancy in signals can be exploited thanks to compressive sensing by means of sparsity in a given domain, random down-sampling the received signal and discarding unnecessary data. However, not all applications are suitable and may not be robust enough. This paper will present preliminary results of passive coherent location and compressive sensing based on signal modelling and Monte-Carlo simulations.
Cristofani, E, Mahfoudia, O, Becquaert, M, Neyt, X, Horlin, F, Deligiannis, N, Stiens, J & Vandewal, M 2017, Compressive Sensing and DVB-T-Based Passive Coherent Location. in 26th URSI Benelux Forum. URSI, pp. 1-2, 26th URSI Benelux Forum, Brusssels, Belgium, 16/02/17. <http://www.sic.rma.ac.be/~ecristof/CS/CristofaniURSI17.pdf>
Cristofani, E., Mahfoudia, O., Becquaert, M., Neyt, X., Horlin, F., Deligiannis, N., Stiens, J., & Vandewal, M. (2017). Compressive Sensing and DVB-T-Based Passive Coherent Location. In 26th URSI Benelux Forum (pp. 1-2). URSI. http://www.sic.rma.ac.be/~ecristof/CS/CristofaniURSI17.pdf
@inproceedings{200e07a6aeb84181bf9f6dad60601944,
title = "Compressive Sensing and DVB-T-Based Passive Coherent Location",
abstract = " For some applications, data burden can become a problem if the system needs to resolve targets accurately. Passive coherent location using digital video broadcast signals is not an exception: large amounts of data need to be acquired, transferred, and stored for processing. Redundancy in signals can be exploited thanks to compressive sensing by means of sparsity in a given domain, random down-sampling the received signal and discarding unnecessary data. However, not all applications are suitable and may not be robust enough. This paper will present preliminary results of passive coherent location and compressive sensing based on signal modelling and Monte-Carlo simulations. ",
author = "Edison Cristofani and Osama Mahfoudia and Mathias Becquaert and Xavier Neyt and Fran{\c c}ois Horlin and Nikolaos Deligiannis and Johan Stiens and Marijke Vandewal",
year = "2017",
month = feb,
day = "16",
language = "English",
pages = "1--2",
booktitle = "26th URSI Benelux Forum",
publisher = "URSI",
note = "26th URSI Benelux Forum ; Conference date: 16-02-2017 Through 16-02-2017",
}