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
Decision making in tracking applications by using dempster-shafer theory /
Download
index.pdf
Date
2014
Author
Turhan, Hasan İhsan
Metadata
Show full item record
Item Usage Stats
201
views
79
downloads
Cite This
The aim of this thesis is to study attribute data fusion and decision making for targets tracked by a sensor network consisting of several radars. As an application deciding both target class and identity are studied. Since only partial information is available, Dempster-Shafer theory is used for this application to assign and combine probability masses. In this study, we focus on the problems of basic probability assignment and decision/data fusion. Classification of air vehicles according to their type is studied using the kinematic features obtained while tracking. The probability masses are obtained from tracker data and prior information that belong to possible target types. Prior information is modeled as a Gaussian mixture probability density function, while tracker data is modeled as a single Gaussian. This new methodology is tested with real data and its performance is examined by comparing it with the most similar method existing in the literature. Special to this type of air vehicle classification problem, a decision fusion approach is proposed that uses Bayesian formalism. The main difference of the proposed methodology from the existing methods is fusing the data before assigning the basic probabilities. Methodology is tested with real data and compared with the existing combination rules in the literature. Target identification is the decision of whether a target is a friend, hostile or neutral. This decision is made by using IFF Mod-4 information, IFF Mod-3 information, restricted area breach information, air corridor usage information and human-eye identification information. These piece of information are converted into probability masses and combined by using Analytic Hierarchy Process Interrogation methods and Dempster-Shafer Theory. Methodology is tested by using artificial scenarios.
Subject Keywords
Tracking (Engineering).
,
Dempster-Shafer theory.
,
Probabilities.
,
Sensor networks.
,
Radar.
URI
http://etd.lib.metu.edu.tr/upload/12617615/index.pdf
https://hdl.handle.net/11511/23984
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Decision and feature fusion over the fractal inference network using camera and range sensors
Erkmen, İsmet; Erkmen, Aydan Müşerref; Ucar, E (1998-11-03)
The objective of the ongoing work is to fuse information from uncertain environmental data taken by cameras, short range sensors including infrared and ultrasound sensors for strategic target recognition and task specific action in Mobile Robot applications. Our present goal in this paper is to demonstrate target recognition for service robot in a simple office environment. It is proposed to fuse all sensory signals obtained from multiple sensors over a fully layer-connected sensor network system that provi...
Construction of an experimental radar system
Kılıçoğlu, Nezaket; Koç, Seyit Sencer; Department of Electrical and Electronics Engineering (2010)
In this thesis, an Experimental Radar System is designed and constructed for use in experimental radar studies such as clutter measurement and target detection, both in the laboratory and outdoor. COTS laboratory equipments are utilized as hardware elements of the radar and MATLAB is used as signal processing and user interface software tool. Vector signal generator (as transmitter), spectrum analyzer with vector signal analysis (as receiver), a high power amplifier, a low noise amplifier, horn antennas and...
Radar emitter emulation for research and experimental purposes.
Çelebi, M. Bahadır; Koç, Seyit Sencer; Department of Electrical and Electronics Engineering (2009)
The scope of this thesis is to implement radar emitter emulator in a low cost, portablehardware for operational and educational purposes. The model enables pulse train generation in real environment belonging to radar emitters for military exercises. The motivation comes from another research area which is to design effective algorithms for deinterleaving mixed pulse sequences in a suitable hardware and this thesis, covers the work done for implementing a hardware that generates mixed pulse sequences. First...
Performance Improvement of Time-Balance Radar Schedulers Through Decision Policies
Çayır, Ömer; Candan, Çağatay (2018-08-01)
The resource management of a phase array system capable of multiple target tracking and surveillance is critical for the realization of its full potential. This paper aims to improve the performance of an existing method, time-balance (TB) scheduling, by establishing an analogy with a well-known stochastic control problem, the machine replacement problem. With the suggested policy, the scheduler can adapt to the operational scenario without a significant sacrifice from the practicality of the TB schedulers....
A Shadow based trainable method for building detection in satellite images
Dikmen, Mehmet; Halıcı, Uğur; Department of Geodetic and Geographical Information Technologies (2014)
The purpose of this thesis is to develop a supervised building detection and extraction algorithm with a shadow based learning method for high-resolution satellite images. First, shadow segments are identified on an over-segmented image, and then neighboring shadow segments are merged by assuming that they are cast by a single building. Next, these shadow regions are used to detect the candidate regions where buildings most likely occur. Together with this information, distance to shadows towards illuminati...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
H. İ. Turhan, “Decision making in tracking applications by using dempster-shafer theory /,” M.S. - Master of Science, Middle East Technical University, 2014.