Multiperson tracking with a network of ultrawideband radar sensors based on gaussian mixture PHD filters
Guldogan, M. B.
IEEE Sensors Journal
Institute of Electrical and Electronics Engineers Inc.
2227 - 2237
Item Usage Stats
MetadataShow full item record
In this paper, we investigate the use of Gaussian mixture probability hypothesis density filters for multiple person tracking using ultrawideband (UWB) radar sensors in an indoor environment. An experimental setup consisting of a network of UWB radar sensors and a computer is designed, and a new detection algorithm is proposed. The results of this experimental proof-of-concept study show that it is possible to accurately track multiple targets using a UWB radar sensor network in indoor environments based on the proposed approach. © 2014 IEEE.
KeywordsMultiple person detection
Communication channels (information theory)
Probability density function
Gaussian mixture phd
Gaussian mixture probability hypothesis density filters
Track multiple targets
Ultra wideband radars
Published Version (Please cite this version)http://dx.doi.org/10.1109/JSEN.2014.2372312
Showing items related by title, author, creator and subject.
Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. Enis (SPIE, 2009-04)In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a ...
Sevimli, R. Akın; Tofighi, Mohammad; Çetin, A. Enis (IEEE, 2014-09)Compressive sensing (CS) idea enables the reconstruction of a sparse signal from a small set of measurements. CS approach has applications in many practical areas. One of the areas is radar systems. In this article, the ...
Duman, Kaan; Çetin, A. Enis (SPIE, 2010)Target detection in SAR images using region covariance (RC) and codifference methods is shown to be accurate despite the high computational cost. The proposed method uses directional filters in order to decrease the search ...