A new neural network approach to the target tracking problem with smart structure

Date

2006-12-01

Author

Caylar, Selcuk
Leblebicioğlu, Mehmet Kemal
Dural, Gülbin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

78
views

0
downloads

The algorithm presented in this paper, namely the modified neural multiple source tracking algorithm (MN-MUST) is the modified form of the recently published work, a NN algorithm, the neural multiple-source tracking (N-MUST) algorithm, was presented for locating and tracking angles of arrival from multiple sources. MN-MUST algorithm consists of three stages that are classified as the detection, filtering and DoA estimation stages. In the first stage a number of radial basis function neural networks (RBFNN) are trained for detection of the angular sectors which have source or sources. A spatial filter stage applied individually to the every angular sector which is classified in the first stage as having source or sources. Each individual spatial filter is designed to filter out the signals coming from all the other angular sectors outside the particular source detected angular sector. This stage considerably improves the performance of the algorithm in the case where more than one angular sector have source or sources at the same time. Insertion of this spatial filtering stage is the main contribution of this paper. The third stage consists of a neural network trained for DoA estimation. In all three stages neural network's size and the training data are considerably reduced as compared to the previous approach, without loss of accuracy

Subject Keywords

Neural networks, Target tracking, Intelligent structures

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=36148970924&origin=inward
https://hdl.handle.net/11511/102174

DOI

https://doi.org/10.1109/aps.2006.1710733

Conference Name

IEEE Antennas and Propagation Society International Symposium, APS 2006

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

A novel neural network based approach for direction of arrival estimation Çaylar, Selçuk; Dural Ünver, Mevlüde Gülbin; Department of Electrical and Electronics Engineering (2007) In this study, a neural network(NN) based algorithm is proposed for real time multiple source tracking problem based on a previously reported work. The proposed algorithm namely modified neural network based multiple source tracking algorithm (MN-MUST) performs direction of arrival(DoA) estimation in three stages which are the detection, filtering and DoA estimation stages. The main contributions of this proposed system are: reducing the input size for the uncorrelated source case (reducing the training tim...
A new neural network approach to the target tracking problem with smart structure Caylar, Selcuk; Leblebicioğlu, Mehmet Kemal; Dural, Guelbin (American Geophysical Union (AGU), 2006-10-03) [1] A modified neural network - based algorithm ( modified neural multiple-source tracking algorithm (MN-MUST)) is proposed for real-time multiple-source tracking problem. The proposed approach reduced the input size of the neural network without any degradation of the accuracy of the system for uncorrelated sources. In addition, a spatial filtering stage that considerably improves the performance of the system is proposed to be inserted. It is observed that the MN-MUST algorithm provides an accurate and ef...
Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference Tuncer, Barkın; Özkan, Emre (2021-02-01) In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear in the state variables, and it is not possible to find a closed-form analytical expression for the true poste...
A Novel Neural Network Method for Direction of Arrival Estimation with Uniform Cylindrical 12-Element Microstrip Patch Array Caylar, Selcuk; Dural, Guelbin; Leblebicioğlu, Mehmet Kemal (2008-01-01) In this study a new neural network algorithm is proposed for real time multiple source tracking problem with cylindrical patch antenna array based on a previous v reported Modified Neural Multiple Source Tracking Algorithm(MN-MUST). The proposed algorithm, namely Cylindrical Microstrip Patch Array Modified Neural Multiple Source Tracking Algorithm (CMN-MUST) implements W-MUST algorithm on a cylindrical microsttip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microstrip...
A neuro-fuzzy MAR algorithm for temporal rule-based systems Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04) This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...

Citation Formats

S. Caylar, M. K. Leblebicioğlu, and G. Dural, “A new neural network approach to the target tracking problem with smart structure,” presented at the IEEE Antennas and Propagation Society International Symposium, APS 2006, Albuquerque, NM, Amerika Birleşik Devletleri, 2006, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=36148970924&origin=inward.