Improving TDOA Radar Performance in Jammed Areas through Neural Network-Based Signal Processing

This paper presents a method for estimating the position of a target under jammed conditions using the Time Difference of Arrival (TDOA) method. The algorithm utilizes a deep neural network to overcome the challenges posed by the jammed conditions. The simulations and results indicate that the prese...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 23; no. 6; p. 2889
Main Authors Gotthans, Jakub, Gotthans, Tomas, Novak, David
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 07.03.2023
MDPI
Subjects
Accuracy
Algorithms
autoencoder
Clocks & watches
Communications equipment
deep neural network
Global positioning systems
GPS
Internet of Things
jamming
Localization
neural network
Neural networks
R&D
radar
Radar systems
Research & development
Satellites
Signal processing
TDOA
Transmitters
correlation method
radar
autoencoder
TDOA
jamming
neural network
deep neural network
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Summary:This paper presents a method for estimating the position of a target under jammed conditions using the Time Difference of Arrival (TDOA) method. The algorithm utilizes a deep neural network to overcome the challenges posed by the jammed conditions. The simulations and results indicate that the presented method is more accurate and efficient than the traditional TDOA methods.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23062889