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
An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation
Download
index.pdf
Date
2013-06-01
Author
Lundquist, Christian
Granstrom, Karl
Orguner, Umut
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
212
views
197
downloads
Cite This
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 been derived by Mahler, and different implementations have been proposed recently. To achieve better estimation performance this work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers. A gamma Gaussian inverse Wishart mixture implementation, which is capable of estimating the target extents and measurement rates as well as the kinematic state of the target, is proposed, and it is compared to its PHD counterpart in a simulation study. The results clearly show that the CPHD filter has a more robust cardinality estimate leading to smaller OSPA errors, which confirms that the extended target CPHD filter inherits the properties of its point target counterpart.
Subject Keywords
Signal Processing
,
Electrical and Electronic Engineering
URI
https://hdl.handle.net/11511/47637
Journal
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
DOI
https://doi.org/10.1109/jstsp.2013.2245632
Collections
Department of Electrical and Electronics Engineering, Article
Suggestions
OpenMETU
Core
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...
Maximum likelihood estimation of transition probabilities of jump Markov linear systems
Orguner, Umut (Institute of Electrical and Electronics Engineers (IEEE), 2008-10-01)
This paper describes an online maximum likelihood estimator for the transition probabilities associated with a jump Markov linear system (JMLS). The maximum likelihood estimator is derived using the reference probability method, which exploits an hypothetical probability measure to find recursions for complex expectations. Expectation maximization (EM) procedure is utilized for maximizing the likelihood function. In order to avoid the exponential increase in the number of statistics of the optimal EM algori...
Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation
Lundquist, Christian; Orguner, Umut; Gustafsson, Fredrik (Institute of Electrical and Electronics Engineers (IEEE), 2011-01-01)
This paper presents an extended target tracking framework which uses polynomials in order to model extended objects in the scene of interest from imagery sensor data. State-space models are proposed for the extended objects which enables the use of Kalman filters in tracking. Different methodologies of designing measurement equations are investigated. A general target tracking algorithm that utilizes a specific data association method for the extended targets is presented. The overall algorithm must always ...
A tracker-aware detector threshold optimization formulation for tracking maneuvering targets in clutter
Aslan, Murat Samil; Saranlı, Afşar (Elsevier BV, 2011-09-01)
In this paper, we consider a tracker-aware radar detector threshold optimization formulation for tracking maneuvering targets in clutter. The formulation results in an online method with improved transient performance. In our earlier works, the problem was considered in the context of the probabilistic data association filter (PDAF) for non-maneuvering targets. In the present study, we extend the ideas in the PDAF formulation to the multiple model (MM) filtering structures which use PDAFs as modules. Althou...
Robust adaptive unscented Kalman filter for attitude estimation of pico satellites
Hacızade, Cengiz; Söken, Halil Ersin (Wiley, 2014-02-01)
Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for the estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurement malfunctions, the UKF becomes inaccurate and diverges by time. This study introduces a fault-tolerant attitude estimation algorithm for pico satellites. The algorithm uses a robust adaptive UKF, which performs correction for the process noise covariance (Q-adapt...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
C. Lundquist, K. Granstrom, and U. Orguner, “An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation,”
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
, pp. 472–483, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47637.