A comparative evaluation of conventional and particle filter based radar target tracking

Download

index.pdf

Date

2007

Author

Yıldırım, Berkin

Metadata

Show full item record

Item Usage Stats

200
views

63
downloads

In this thesis the radar target tracking problem in Bayesian estimation framework is studied. Traditionally, linear or linearized models, where the uncertainty in the system and measurement models is typically represented by Gaussian densities, are used in this area. Therefore, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo methods, i.e. particle filters, make it possible to utilize the inherent non-linear state relations and non-Gaussian noise models. Given the sufficient computational power, the particle filter can provide better results than Kalman filter based methods in many cases. A survey over relevant radar tracking literature is presented including aspects as estimation and target modeling. In various target tracking related estimation applications, particle filtering algorithms are presented.

Subject Keywords

Electrical engineering.

URI

http://etd.lib.metu.edu.tr/upload/12609043/index.pdf
https://hdl.handle.net/11511/17327

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Quantization based data hiding strategies with visual applications Esen, Ersin; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2010) The first explored area in this thesis is the proposed data hiding method, TCQ-IS. The method is based on Trellis Coded Quantization (TCQ), whose initial state selection is arbitrary. TCQ-IS exploits this fact to hide data. It is a practical multi-dimensional that eliminates the prohibitive task of designing high dimensional quantizers. The strength and weaknesses of the method are stated by various experiments. The second contribution is the proposed data hiding method, Forbidden Zone Data Hiding (FZDH), w...
Dynamic system modeling and state estimation for speech signal Özbek, İbrahim Yücel; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2010) This thesis presents an all-inclusive framework on how the current formant tracking and audio (and/or visual)-to-articulatory inversion algorithms can be improved. The possible improvements are summarized as follows: The first part of the thesis investigates the problem of the formant frequency estimation when the number of formants to be estimated fixed or variable respectively. The fixed number of formant tracking method is based on the assumption that the number of formant frequencies is fixed along the ...
Visual Result Prediction in Electromagnetic Simulations Using Machine Learning Karaosmanoglu, Bariscan; Ergül, Özgür Salih (Institute of Electrical and Electronics Engineers (IEEE), 2019-11-01) In this letter, we present a novel approach based on using convolutional neural networks (CNNs) to visually predict solutions of electromagnetic problems. CNN models are constructed and trained such that images of surface currents obtained at the early stages of an iterative solution can be used to predict images of the final (converged) solution. Numerical experiments demonstrate that the predicted images contain significantly better visual details than the corresponding input images. The developed approac...
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 ...
A Hierarchical Partitioning Strategy for an Efficient Parallelization of the Multilevel Fast Multipole Algorithm Ergül, Özgür Salih (Institute of Electrical and Electronics Engineers (IEEE), 2009-06-01) We present a novel hierarchical partitioning strategy for the efficient parallelization of the multilevel fast multipole algorithm (MLFMA) on distributed-memory architectures to solve large-scale problems in electromagnetics. Unlike previous parallelization techniques, the tree structure of MLFMA is distributed among processors by partitioning both clusters and samples of fields at each level. Due to the improved load-balancing, the hierarchical strategy offers a higher parallelization efficiency than previ...

Citation Formats

B. Yıldırım, “A comparative evaluation of conventional and particle filter based radar target tracking,” M.S. - Master of Science, Middle East Technical University, 2007.