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
Multitarget tracking performance metric: deficiency aware subpattern assignment
Date
2018-03-01
Author
Oksuz, Kemal
CEMGİL, ALİ TAYLAN
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
0
downloads
Cite This
Multitarget tracking is a sequential estimation problem where conditioned on noisy sensor measurements, state variables of several targets need to be estimated recursively. In this study, the authors propose a novel performance measure for multitarget tracking named as Deficiency Aware Subpattern Assignment (DASA), that can be used to consistently compare algorithms in a broad spectrum of formulations ranging from conventional data association methods to random finite set based multitarget tracking algorithms. The DASA metric combines three components (localisation, type 1 and type 2 errors) in order to represent the behaviour of the tracking filter coherently. Furthermore, a Monte Carlo method is proposed in order to set the cut-off parameter for the case that the measurement model is known. They illustrate in their simulations that DASA improves upon the previously proposed Optimal Subpattern Assignment metric.
Subject Keywords
Sensor fusion
,
Tracking filters
,
Target tracking
,
State variables
,
Performance measure
,
Deficiency aware subpattern assignment
,
Optimal Subpattern Assignment metric
,
Tracking filter
,
DASA metric combines three components
,
Multitarget tracking algorithms
,
Conventional data association methods
,
Noisy sensor measurements
,
Sequential estimation problem
,
Multitarget tracking performance metric
URI
https://hdl.handle.net/11511/65599
Journal
IET RADAR SONAR AND NAVIGATION
DOI
https://doi.org/10.1049/iet-rsn.2017.0390
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Residual based Adaptive Unscented Kalman filter for satellite attitude estimation
Söken, Halil Ersin (2012-12-01)
Determining the process noise covariance matrix in Kalman filtering applications is a difficult task especially for estimation problems of the high-dimensional states where states like biases or system parameters are included. This study introduces a simplistic residual based adaptation method for the Unscented Kalman Filter (UKF), which is used for small satellite attitude estimation. For a satellite with gyros and magnetometers onboard, the proposed adaptive UKF algorithm estimates the attitude as well as...
Distributed Target Tracking with Propagation Delayed Measurements
Orguner, Umut (2009-07-09)
This paper presents a framework for making distributed target tracking under significant signal propagation delays between the target and the sensors. Each sensor considered makes estimation using its own measurements compensating for the involved signal propagation delay using a deterministic sampling based algorithm proposed previously. Since the individual sensor readings might not be enough to localize the target, the sensors have to share their estimates with each other at specific time instants and co...
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.
Multi-target tracking with PHD filter using Doppler-only measurements
Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut (2014-04-01)
In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation...
Fine resolution frequency estimation from three DFT samples: Case of windowed data
Candan, Çağatay (2015-09-01)
An efficient and low complexity frequency estimation method based on the discrete Fourier transform (DFT) samples is described. The suggested method can operate with an arbitrary window function in the absence or presence of zero-padding. The frequency estimation performance of the suggested method is shown to follow the Cramer-Rao bound closely without any error floor due to estimator bias, even at exceptionally high signal-to-noise-ratio (SNR) values.
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
K. Oksuz and A. T. CEMGİL, “Multitarget tracking performance metric: deficiency aware subpattern assignment,”
IET RADAR SONAR AND NAVIGATION
, pp. 373–381, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65599.