Multi‐sensor data fusion between radio tomographic imaging and noise radar. Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: Vergara, Christopher; Martin, Richard K.; Collins, Peter J.; Lievsay, James R.
• Is Part Of: IET radar, sonar & navigation. Volume 14:Issue 2(2020)
• Page Start: 187
• Page End: 193
• Publication Date: 2020-02-01
• Subjects: sensor fusion -- surveillance -- radar imaging -- image resolution; noise radar communities -- multisensor data fusion -- radio tomographic imaging -- Tikhonov regularisation -- target centroid location -- target pixel dispersion -- ideal solution comparison -- disparate sensor technologies; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2019.0092
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Radio tomographic imaging and noise radar are two proven surveillance technologies. The novelty of fusing data from radio tomographic imaging and noise radar is achieved with the derivation of a fusion technique utilising Tikhonov regularisation. Analysing the results of the Tikhonov influenced techniques reveals an average 43–47% error decrease in target centroid location, a 13–19% size decreases in target pixel dispersion and a 6–41% improvement in an ideal solution comparison. Results provide the radio tomographic imaging and noise radar communities a proof of concept for the fusion of data from two disparate sensor technologies.