Title
Novi pristup u proceni pravca dolazećeg EM signala zasnovan na primeni veštačkih neuronskih mreža
Creator
Stoilković, Marija M.
Copyright date
2014
Object Links
Select license
Autorstvo-Nekomercijalno 3.0 Srbija (CC BY-NC 3.0)
License description
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Language
Serbian
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 26.06.2015.
Other responsibilities
mentor
Milovanović, Branislav
član komisije
Dončov, Nebojša, 1970-
član komisije
Marković, Vera 1956-
član komisije
Jokanović, Branka
član komisije
Maleš-Ilić, Nataša 1968-
član komisije
Pronić-Rančić, Olivera 1969-
Academic Expertise
Tehničko-tehnološke nauke
University
Univerzitet u Nišu
Faculty
Elektronski fakultet
Group
Katedra za telekomunikacije
Alternative title
New approach in direction of arrival estimation of EM signals using artificial neutral networks
Publisher
[M. M. Stoilković]
Format
[16],150 listova
description
Telecommunications, Artificial neural networks
Abstract (en)
Direction of Arrival (DOA) estimation of electromagnetic (EM) signals strongly
depends on characteristics of received signals, configuration and physical layout of a
receiving antenna array and environmental conditions. DOA algorithms have crucial role in
adaptive antenna systems where used to provide better reception of desired signals and to
suppress interference. Consequently, they have significant impact on system capacity. So far,
numerous DOA algorithms have been developed assuming different configurations of
antenna arrays and characteristics of EM signals. The main problem that appears when these
algorithms are applied in practice is that a compromise between accuracy of the results and
speed of calculation must be made. For that reason, a method for DOA estimation based on
Artificial Neural Networks (ANNs) is applied in this dissertation.
The main contribution of this work is development of neural models for the efficient
and accurate DOA estimation of EM signals both in azimuth and elevation. Assuming
rectangular antenna array geometry at the receiver, neural models for 2D DOA estimation of
uncorrelated and coherent signals are developed. It is shown that applying a hierarchical
neural model can greatly affect the accuracy of DOA estimates when compared to standard
superresolution algorithms. To develop neural models, the observed space is divided into
sectors in azimuth and elevation. The neural networks in the first stage of the model are
trained to detect presence of electromagnetic sources, while the networks in the second stage
are trained to provide precise DOA estimates. The appropriately trained hierarchical model,
composed of smaller neural networks, can be used for accurate DOA estimation in azimuth
and elevation. As follows, time to train separate networks is drastically reduced as different
training sets are used for the networks in the detection and estimation stage. The proposed
models are very efficient, able to provide DOA estimates in a matter of seconds and very
suitable for real time application. Performances of the models are experimentally verified.
Besides, the results are compared with the results of the standard algorithm for DOA
estimation. Advantages of the proposed models are accuracy of DOA estimates and speed of
calculation. In addition, neural models can be trained to account for some other
characteristics of EM signals (number of signals, signal to noise ratio, correlation between
signals), physical characteristics of the receiving array and environmental conditions.
Influence of signal to noise ratio (SNR) on the performance of neural models, aimed
for DOA estimation of signals in azimuth plane, is also investigated. It is shown that SNR
strongly affects the accuracy of DOA estimates, and as such should be involved as an
additional parameter in the process of training of a neural network. Further, detection of
correlated signals is investigated assuming circular antenna array geometry. In this case,
neural models are developed for different number of signals and correlation between them.
In this dissertation, application of artificial neural networks in DOA estimation of
radar MIMO (Multiple Input Multiple Output) – OFDM (Orthogonal Frequency Division
Multiplexing) signals is presented. A neural model is optimized and its performance is
compared to conventional algorithm regarding accuracy and time consumption.
Authors Key words
antenski nizovi, MUSIC algoritam, procena pravca EM signala, prostorna
kovarijansna matrica, prostorna obrada signala, veštačke neuronske mreže
Authors Key words
antenna arrays, artificial neural networks, DOA estimation, MUSIC algorithm,
spatial covariance matrix, spatial signal processing
Classification
621.391:004.032.26(043.3)
Type
Elektronska teza
Abstract (en)
Direction of Arrival (DOA) estimation of electromagnetic (EM) signals strongly
depends on characteristics of received signals, configuration and physical layout of a
receiving antenna array and environmental conditions. DOA algorithms have crucial role in
adaptive antenna systems where used to provide better reception of desired signals and to
suppress interference. Consequently, they have significant impact on system capacity. So far,
numerous DOA algorithms have been developed assuming different configurations of
antenna arrays and characteristics of EM signals. The main problem that appears when these
algorithms are applied in practice is that a compromise between accuracy of the results and
speed of calculation must be made. For that reason, a method for DOA estimation based on
Artificial Neural Networks (ANNs) is applied in this dissertation.
The main contribution of this work is development of neural models for the efficient
and accurate DOA estimation of EM signals both in azimuth and elevation. Assuming
rectangular antenna array geometry at the receiver, neural models for 2D DOA estimation of
uncorrelated and coherent signals are developed. It is shown that applying a hierarchical
neural model can greatly affect the accuracy of DOA estimates when compared to standard
superresolution algorithms. To develop neural models, the observed space is divided into
sectors in azimuth and elevation. The neural networks in the first stage of the model are
trained to detect presence of electromagnetic sources, while the networks in the second stage
are trained to provide precise DOA estimates. The appropriately trained hierarchical model,
composed of smaller neural networks, can be used for accurate DOA estimation in azimuth
and elevation. As follows, time to train separate networks is drastically reduced as different
training sets are used for the networks in the detection and estimation stage. The proposed
models are very efficient, able to provide DOA estimates in a matter of seconds and very
suitable for real time application. Performances of the models are experimentally verified.
Besides, the results are compared with the results of the standard algorithm for DOA
estimation. Advantages of the proposed models are accuracy of DOA estimates and speed of
calculation. In addition, neural models can be trained to account for some other
characteristics of EM signals (number of signals, signal to noise ratio, correlation between
signals), physical characteristics of the receiving array and environmental conditions.
Influence of signal to noise ratio (SNR) on the performance of neural models, aimed
for DOA estimation of signals in azimuth plane, is also investigated. It is shown that SNR
strongly affects the accuracy of DOA estimates, and as such should be involved as an
additional parameter in the process of training of a neural network. Further, detection of
correlated signals is investigated assuming circular antenna array geometry. In this case,
neural models are developed for different number of signals and correlation between them.
In this dissertation, application of artificial neural networks in DOA estimation of
radar MIMO (Multiple Input Multiple Output) – OFDM (Orthogonal Frequency Division
Multiplexing) signals is presented. A neural model is optimized and its performance is
compared to conventional algorithm regarding accuracy and time consumption.
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