Tracking of ground targets with interacting multiple model estimator

Download

index.pdf

Date

2011

Author

Acar, Duygu

Metadata

Show full item record

Item Usage Stats

149
views

129
downloads

Interacting Multiple Model (IMM) estimator is used extensively to estimate trajectories of maneuvering targets in cluttered environment. In the standard tracking methods, it is assumed that movement of target is applicable to a certain model and the target could be monitored via the usage of status predictions of that model. However, targets can make different maneuvering movements. At that time, expression of target dynamic model with only one model can be insufficient. In IMM approach, target dynamic model is expressed with more than one model capsulating all maneuvering movements or with one model with different noise level values. This thesis investigates the tracking of the maneuvering ground targets in cluttered environment via IMM estimator with constant velocity model with low/high process noise, coordinated turn model and move-stop-move model. The selection strategies of models are highlighted and the state errors are calculated to evaluate the performance of IMM estimator.

Subject Keywords

Electronic systems.

URI

http://etd.lib.metu.edu.tr/upload/12613967/index.pdf
https://hdl.handle.net/11511/21039

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Acceleration of line of sight analysis algorithms with parallel programming Yılmaz, Gökhan; Sürer, Elif; Temizel, Alptekin (null; 2017-11-23) Line of sight (LOS) analysis is a set of methods and algorithms to determine the visible points in a terrain with reference to a specific observer point. This analysis is used in simulations, Geographic Information System (GIS) applications and games. For this reason, it is important to have a capability to get results quickly and facilitate analysis in such a way that the interaction with the changing reference points is possible. Van Kreveld, R2 and R3 are the most frequently used algorithms in line of si...
FMCW radar altimeter test board Vural, Aydın; Hızal, Altunkan; Department of Electrical and Electronics Engineering (2003) In this thesis, principles of a pulse modulated frequency modulated continuous wave radar is analyzed and adding time delay to transmitted signal in the laboratory environment performed. The transmitted signal from the radar has a time delay for traveling the distance between radar and target. The distance from radar to target is more than one kilometers thus test of the functionality of the radar in the laboratory environment is unavailable. The delay is simulated regarding to elapsed time for the transmit...
Learning cooperation in hunter-prey problem via state abstraction İşçen, Atıl; Polat, Faruk; Department of Computer Engineering (2009) Hunter-Prey or Prey-Pursuit problem is a common toy domain for Reinforcement Learning, but the size of the state space is exponential in the parameters such as size of the grid or number of agents. As the size of the state space makes the flat Q-learning impossible to use for different scenarios, this thesis presents an approach to make the size of the state space constant by producing agents that use previously learned knowledge to perform on bigger scenarios containing more agents. Inspired from HRL metho...
Direction finding accuracy of sequential lobing under target amplitude fluctuations Candan, Çağatay (Institution of Engineering and Technology (IET), 2015-01-01) Using recently developed statistical target fluctuation models, the accuracy of sequential lobing is analytically studied. The study shows that the sequential lobing method suffers from a significant performance loss, in comparison with the monopulse method, for the Rayleigh fluctuation model. For other fluctuation models, the performance loss gradually decreases as the amplitude spread associated with the fluctuation gets smaller. The present study aims to analytically quantify the mentioned accuracy loss ...
Towards finding optimal mixture of subspaces for data classification Musa, Mohamed Elhafiz Mustafa; Atalay, Mehmet Volkan; Department of Computer Engineering (2003) In pattern recognition, when data has different structures in different parts of the input space, fitting one global model can be slow and inaccurate. Learning methods can quickly learn the structure of the data in local regions, consequently, offering faster and more accurate model fitting. Breaking training data set into smaller subsets may lead to curse of dimensionality problem, as a training sample subset may not be enough for estimating the required set of parameters for the submodels. Increasing the ...

Citation Formats

D. Acar, “Tracking of ground targets with interacting multiple model estimator,” M.S. - Master of Science, Middle East Technical University, 2011.