Small Target Detection in a Radar Surveillance System Using Contractive Autoencoders

With the rapid development of unpiloted aerial vehicles (UAVs), also known as drones, in recent years, the need for surveillance systems that are able to detect drones has grown as well. Radar is the technology with the potential to fulfill this task, and several previous publications show examples...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 60; no. 1; pp. 51 - 67
Main Authors: Wagner, Simon, Johannes, Winfried, Qosja, Denisa, Bruggenwirth, Stefan
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Autoencoder
Background noise
Chirp
Drone aircraft
drone detection
Drone vehicles
Drones
False alarms
Machine learning
Radar
Radar antennas
Radar cross-sections
Radar detection
radar surveillance
Surveillance
Surveillance radar
Surveillance systems
Target detection
Unmanned aerial vehicles
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Summary:With the rapid development of unpiloted aerial vehicles (UAVs), also known as drones, in recent years, the need for surveillance systems that are able to detect drones has grown as well. Radar is the technology with the potential to fulfill this task, and several previous publications show examples of radar detection and classification schemes. The purpose of this article is related to the detection scheme used in these approaches. Most surveillance systems use a background subtraction and a threshold to detect targets. This threshold often depends on a model of the radar noise and the background, which is imperfect by nature. The approach presented here uses a data-driven machine learning algorithm that is trained with measured background profiles of the radar and is applied afterward to the given background for target detection. This scheme can in general be applied to any detection problem in a fixed area, but is shown here with examples from measurements of drones and persons. The results show that the chosen approach gives better detection rates for low false alarm rates with real data than that given by background subtraction.
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2023.3253469