Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field

Stankovic, Zoran; Dončov, Nebojša; Milovanović, Ivan; Milovanović, Bratislav

doi:10.1109/neurel.2014.7011455

Cited by 9 publications

(22 citation statements)

References 5 publications

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“…In this paper, the same model of stochastic EM source radiation in a far-field zone, as in [9][10][11], is used. Based on this model, a radiation of a number of stochastic EM sources in far-field is described by a radiation of short dipoles.…”

Section: Stochastic Em Source Radiation Modelmentioning

confidence: 99%

“…Its MLP network can be described by the following function: (10) where y l -1 vector represents the output of (l-1)-th hidden layer, wl is a connection weight matrix among (l-1)-th and l-th hidden layer neurons, b l is a vector containing biases of l-th hidden layer neurons and H is number of hidden layers. F is the activation function of neurons in hidden layers and in this case it is a hyperbolic tangent sigmoid transfer function: The general designation for this defined MLP neural model is MLPH-N 1 -…-Ni-…-N H where H is the total number of hidden layers used in MLP network, while Ni is the total number of neurons in the i-th hidden layer.…”

Section: Neural Network Modelmentioning

confidence: 99%

“…These neural solutions may achieve same accuracy while gaining higher speeds in response. This is shown in [5][6][7][8][9][10][11], with a particular emphasis on neural models for 1D DoA estimation [6,7] and neural model for 2D DoA estimation of deterministic radiation sources [8].…”

Section: Introductionmentioning

confidence: 99%

“…In [9][10][11] the neural models for 1D DoA [9,10] and 2D DoA estimation [11] and spatial position estimation of stochastic radiation sources are presented, developed for sources whose radiation is mutually uncorrelated. This paper continues research work of [9,10] with the aim to investigate the possibility of multilayer perceptron neural network (MLP) application (MLP) [3][4][5] in DoA estimation for stochastic radiation sources for the case when there is some level of partial correlation in their radiations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Neural Model for Far-Field 1D Localization of Mobile Stochastic EM Sources with Partially Correlated Radiation

Milovanović¹

2017

Proceedings of the International Scientific Conference - Sinteza 2017

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Abstract:This paper considers a possibility of angle position determination of mobile stochastic sources with partially correlated radiation, where antenna array and multilayer perceptron neural network processing are used. It is shown that the neural model trained with samples from a system with uncorrelated source radiation cannot determine position of sources with a satisfactory accuracy when sources have some degree of correlation in radiation. That is why it is suggested training samples to be generated for different values of partial source correlation. This kind of generated neural network training may provide source position determination with satisfactory accuracy even when there is partial correlation, which in the paper is presented with an example of two sources that linearly move in azimuth plane

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Section: Stochastic Em Source Radiation Modelmentioning

confidence: 99%

Section: Neural Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Model for Far-Field 1D Localization of Mobile Stochastic EM Sources with Partially Correlated Radiation

Milovanović¹

2017

Proceedings of the International Scientific Conference - Sinteza 2017

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“…However, because of its complex matrix calculation requires powerful hardware resources, they are not suitable for real-time operations. A good alternative to the superresolution algorithms, the application of artificial neural networks [5][6][7] to solve problems DoA where neural models that avoid complex matrix calculations can have an approximate accuracy of the MUSIC algorithm without having to be significantly faster than the MUSIC algorithm which makes them more suitable choice for deployment in real time [8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Model for Eficient Estimation of the Number of Mobile Stochastic EM Sources in a Space Sector

Stankovic¹,

Milovanović

2016

Proceedings of the International Scientific Conference - Sinteza 2016

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Abstract:Information on the total number of radiation sources that are currently observed in the physical sector may be of use in procedures dealing with efficient DoA (Direction of Arrival) estimation of stochastic radiation source. This paper introduces an artificial neural model based on MLP (Multi-Layer Perceptron) network, that is based on a value of the spatial correlation matrix signal sampled in the far zone ofradiation, and can determine in real time, the number of mobile stochastic sources (up to five sources) with mutually uncorrelated radiation that are currently present in the given sector in the azimuthal plane. This model is an upgrade MLP model that previously had implementation for a maximum of three sources in the sector. Training neural model was carried out on the samples of the spatial correlation matrix obtained by using Green's functions. The authors observe the case when the number of sources in the sector variable in time and when the sources during the movement in the given sector may be located in another arbitrary distance.

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Direction of arrival estimation of mobile stochastic electromagnetic sources with variable radiation powers using hierarchical neural model

Stankovic

Dončov

Milovanović

et al. 2019

Int J RF Microw Comput Aided Eng

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Hierarchical neural model for an efficient one‐dimensional direction of arrival (DoA) estimation of stochastic electromagnetic (EM) sources with a variable radiation power is proposed. Model is trained to provide an azimuth position of such sources based on a spatial correlation matrix obtained by a signal sampling at a reception point and then used as an input to a neural model. It consists of two hierarchical levels realized by the multilayer perceptron (MLP)‐based neural networks. The first level is responsible to reduce the dimensionality of the considered DoA problem which allows for its easier solution at the second level. Accuracy and run‐time of the proposed model is verified on an example of determining the azimuth position of two stochastic EM sources in noisy conditions through comparison with a standalone MLP model and a model based on the root MUSIC algorithm.

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Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field

Cited by 9 publications

References 5 publications

Neural Model for Far-Field 1D Localization of Mobile Stochastic EM Sources with Partially Correlated Radiation

Neural Model for Far-Field 1D Localization of Mobile Stochastic EM Sources with Partially Correlated Radiation

An Artificial Neural Network Model for Eficient Estimation of the Number of Mobile Stochastic EM Sources in a Space Sector

Direction of arrival estimation of mobile stochastic electromagnetic sources with variable radiation powers using hierarchical neural model

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