Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction
In this paper, the Direction of Arrival - DoA estimation for two mobile sources was performed by using the Single Multilayer Perceptron (MLP) neural network model (SMLP-DoA) and the Cascade MLP model(CMLP). The latter model consists of two neural networks connected in a cascade where the outputs of...
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| Format: | Article |
| Language: | English |
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Spolecnost pro radioelektronicke inzenyrstvi
2023-09-01
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| Series: | Radioengineering |
| Subjects: | |
| Online Access: | https://www.radioeng.cz/fulltexts/2023/23_03_0381_0390.pdf |
| _version_ | 1827820268630835200 |
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| author | M. Ilic Z. Stankovic N. Males-Ilic |
| author_facet | M. Ilic Z. Stankovic N. Males-Ilic |
| author_sort | M. Ilic |
| collection | DOAJ |
| description | In this paper, the Direction of Arrival - DoA estimation for two mobile sources was performed by using the Single Multilayer Perceptron (MLP) neural network model (SMLP-DoA) and the Cascade MLP model(CMLP). The latter model consists of two neural networks connected in a cascade where the outputs of the first MLP that rejects noise represent the inputs to the second network in a cascade. The outputs of the neural network models determine the direction of arrival of the incoming signals. Two cases were considered, in the first case the neural networks were trained on the samples that were without noise, and in the second with samples containing noise. Both considered neural network models were tested with noisy samples. The results of these two neural models are compared to the results achieved by the RootMUSIC algorithm. The presented results show that the proposed CMLP model has a higher accuracy in determining the angular positions of sources compared to the classical SMLP-DoA model and the RootMUSIC algorithm. Moreover, the CMLP model executes significantly faster compared to the model based on the RootMUSIC algorithm. |
| first_indexed | 2024-03-12T01:26:30Z |
| format | Article |
| id | doaj.art-a9b7514daea94061985a9ced58006310 |
| institution | Directory Open Access Journal |
| issn | 1210-2512 |
| language | English |
| last_indexed | 2024-03-12T01:26:30Z |
| publishDate | 2023-09-01 |
| publisher | Spolecnost pro radioelektronicke inzenyrstvi |
| record_format | Article |
| series | Radioengineering |
| spelling | doaj.art-a9b7514daea94061985a9ced580063102023-09-12T18:28:13ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122023-09-01323381390Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise ReductionM. IlicZ. StankovicN. Males-IlicIn this paper, the Direction of Arrival - DoA estimation for two mobile sources was performed by using the Single Multilayer Perceptron (MLP) neural network model (SMLP-DoA) and the Cascade MLP model(CMLP). The latter model consists of two neural networks connected in a cascade where the outputs of the first MLP that rejects noise represent the inputs to the second network in a cascade. The outputs of the neural network models determine the direction of arrival of the incoming signals. Two cases were considered, in the first case the neural networks were trained on the samples that were without noise, and in the second with samples containing noise. Both considered neural network models were tested with noisy samples. The results of these two neural models are compared to the results achieved by the RootMUSIC algorithm. The presented results show that the proposed CMLP model has a higher accuracy in determining the angular positions of sources compared to the classical SMLP-DoA model and the RootMUSIC algorithm. Moreover, the CMLP model executes significantly faster compared to the model based on the RootMUSIC algorithm.https://www.radioeng.cz/fulltexts/2023/23_03_0381_0390.pdfthe direction of arriva (doa) estimationartificial neural networksmultilayer perceptron (mlp)single mlpcascade mlprootmusic algorithm |
| spellingShingle | M. Ilic Z. Stankovic N. Males-Ilic Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction Radioengineering the direction of arriva (doa) estimation artificial neural networks multilayer perceptron (mlp) single mlp cascade mlp rootmusic algorithm |
| title | Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction |
| title_full | Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction |
| title_fullStr | Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction |
| title_full_unstemmed | Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction |
| title_short | Spatial Localization of Electromagnetic Radiation Sources by Cascade Neural Network Model with Noise Reduction |
| title_sort | spatial localization of electromagnetic radiation sources by cascade neural network model with noise reduction |
| topic | the direction of arriva (doa) estimation artificial neural networks multilayer perceptron (mlp) single mlp cascade mlp rootmusic algorithm |
| url | https://www.radioeng.cz/fulltexts/2023/23_03_0381_0390.pdf |
| work_keys_str_mv | AT milic spatiallocalizationofelectromagneticradiationsourcesbycascadeneuralnetworkmodelwithnoisereduction AT zstankovic spatiallocalizationofelectromagneticradiationsourcesbycascadeneuralnetworkmodelwithnoisereduction AT nmalesilic spatiallocalizationofelectromagneticradiationsourcesbycascadeneuralnetworkmodelwithnoisereduction |