Experimental validation of a neural network direction finder

Southall, H.L.; Christodoulou, C.G.

doi:10.1109/aps.1999.788249

Cited by 13 publications

(7 citation statements)

References 4 publications

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“…In References and , neural models based only on the radial basis function (RBF) neural network were suggested, in References RBF neural model from References and was enhanced with the introduction of modular structure and division of the monitoring space into a number of smaller sectors, while in Reference , a hybrid RBF‐AML model for the DoA estimation was presented aiming to combine good properties of ML method and pure RBF approach. In all these references, neural models were applied for the DoA estimation of deterministic signals radiated from mobile electromagnetic (EM) sources.…”

Section: Introductionmentioning

confidence: 99%

“…1,[10][11][12] Artificial neural networks (ANNs) [13][14][15][16] represent an another direction of research for solving the DoA problems in real-time. [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] Beside ANNs, the more general methods in the field of artificial intelligence, such as genetic programming (GP) and support-vector machines (SVM), are used in today research for the same purpose, as demonstrated for the DoA estimation problem solving in Reference 35 and Reference 36, respectively. Although the GP and SVM-based DoA models have more generalization capabilities than the ANN models in modeling highlynonlinear dependences present in DoA problems, DoA ANN models still represent a good alternative.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…In recent decades, neural network (NN) methods for localization have been studied intensively, with radial basis function (RBF) networks [ 1 , 2 , 3 , 4 ] and multilayer perceptron (MLP) networks [ 5 , 6 , 7 , 8 ] applied for localization. For real-time direction-of-arrival (DOA) estimation problems, a minimal resource allocation network (MRAN) for DOA estimation under array sensor failure in a noisy environment has been developed [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation

Chen

Wang

Yin

et al. 2018

Sensors

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The most widely used localization technology is the two-step method that localizes transmitters by measuring one or more specified positioning parameters. Direct position determination (DPD) is a promising technique that directly localizes transmitters from sensor outputs and can offer superior localization performance. However, existing DPD algorithms such as maximum likelihood (ML)-based and multiple signal classification (MUSIC)-based estimations are computationally expensive, making it difficult to satisfy real-time demands. To solve this problem, we propose the use of a modular neural network for multiple-source DPD. In this method, the area of interest is divided into multiple sub-areas. Multilayer perceptron (MLP) neural networks are employed to detect the presence of a source in a sub-area and filter sources in other sub-areas, and radial basis function (RBF) neural networks are utilized for position estimation. Simulation results show that a number of appropriately trained neural networks can be successfully used for DPD. The performance of the proposed MLP-MLP-RBF method is comparable to the performance of the conventional MUSIC-based DPD algorithm for various signal-to-noise ratios and signal power ratios. Furthermore, the MLP-MLP-RBF network is less computationally intensive than the classical DPD algorithm and is therefore an attractive choice for real-time applications.

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“…With these remarks in mind, the source location problem should be overcome. The numerous approaches and techniques are known for detecting, identifying and classifying various types of disturbance sources [1]- [6]. Nevertheless, the compact broadband mobile direction-finders with high-rapid rates are required.…”

Section: Introductionmentioning

confidence: 99%