Publication result detail

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

GÖTTHANS, J.; GÖTTHANS, T.; NOVÁK, D.

Original Title

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

English Title

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

Type

WoS Article

Original Abstract

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.

English abstract

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.

Keywords

TDOA; radar; autoencoder; neural network; deep neural network; jamming; correlation method

Key words in English

TDOA; radar; autoencoder; neural network; deep neural network; jamming; correlation method

Authors

GÖTTHANS, J.; GÖTTHANS, T.; NOVÁK, D.

RIV year

2024

Released

23.03.2023

Publisher

MDPI

Location

BASEL

ISBN

1424-8220

Periodical

SENSORS

Volume

23

Number

6

State

Swiss Confederation

Pages from

1

Pages to

21

Pages count

21

URL

https://www.mdpi.com/1424-8220/23/6/2889

BibTex

@article{BUT184348,
  author="Jakub {Götthans} and Tomáš {Götthans} and David {Novák}",
  title="Improving TDOA Radar Performance in Jammed Areas through Neural Network-Based Signal Processing",
  journal="SENSORS",
  year="2023",
  volume="23",
  number="6",
  pages="1--21",
  doi="10.3390/s23062889",
  url="https://www.mdpi.com/1424-8220/23/6/2889"
}