Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Multiple target tracking with Gaussian mixture PHD filter using passive acoustic Doppler-only measurements
Date
2012-09-12
Author
Guldogan, Mehmet B.
Lindgren, David
Gustafsson, Fredrik
Habberstad, Hans
Orguner, Umut
Metadata
Show full item record
Item Usage Stats
183
views
0
downloads
Cite This
In this paper, we present the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple ground targets using a passive acoustic-sensor network. For this purpose, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Non-cooperative transmissions from a loudspeaker (i.e. illuminator of opportunity) are exploited by non-directional separately located microphones (i.e. Doppler measuring sensors). Experimental proof-of-concept study results show that it is possible to track multiple ground targets using only Doppler shift measurements in a passive multi-static scenario. © 2012 ISIF (Intl Society of Information Fusi).
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84867654091&origin=inward
https://hdl.handle.net/11511/80520
15th International Conference on Information Fusion, FUSION (2012)
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Multi-target tracking using passive doppler measurements
Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26)
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements
Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2012-05-17)
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target s...
Extended target tracking with a cardinalized probability hypothesis density filter
Orguner, Umut; Granström, Karl (null; 2011-07-08)
This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is d...
MULTITARGET PARTICLE FILTER BASED TRACK BEFORE DETECT ALGORITHM FOR TRACKING OF SPAWNING TARGETS
Eyili, Mehmet; Demirekler, Mübeccel (2014-04-25)
In this paper, two proposed Track Before Detect (TBD) algorithms for spawning targets on the basis of raw radar measurements are described. These algorithms are developed by using multi-model particle filter method. To improve the efficiency of particle filter a novel reduced order model is introduced. The algorithms are confirmed by using the simulation results and their performances are analyzed on the basis of the probability of target existence and Root Mean Square (RMS) estimation accuracy for very low...
RECONSTRUCTION OF PERMITTIVITY AND CONDUCTIVITY PROFILES OF A DIELECTRIC SLAB IN THE TIME DOMAIN BY DESCENT METHODS
ONDER, M; Kuzuoğlu, Mustafa (Institution of Engineering and Technology (IET), 1992-10-01)
An optimisation approach is presented for the problem of reconstructing the permittivity and conductivity profiles of a dielectric slab from the reflected and transmitted field data. The problem is treated as an optimal control problem where the norm of the difference of measured and calculated boundary data is minimised subject to the state equation governing the system. The original constrained optimisation problem is reduced to the evaluation of stationary points of an augmented functional which is obtai...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. B. Guldogan, D. Lindgren, F. Gustafsson, H. Habberstad, and U. Orguner, “Multiple target tracking with Gaussian mixture PHD filter using passive acoustic Doppler-only measurements,” Singapore, Singapur, 2012, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84867654091&origin=inward.