Bounded-error target localization and tracking using a fleet of UAVs

Automatica ◽  
2021 ◽  
Vol 132 ◽  
pp. 109809
Author(s):  
Julius Ibenthal ◽  
Michel Kieffer ◽  
Luc Meyer ◽  
Hélène Piet-Lahanier ◽  
Sébastien Reynaud
Keyword(s):  
Target Localization ◽  
Bounded Error ◽  
Localization And Tracking

scholarly journals Bounded-error target localization and tracking in presence of decoys using a fleet of UAVs

2020 ◽  
Vol 53 (2) ◽  
pp. 9521-9528
Author(s):  
Julius Ibenthal ◽  
Luc Meyer ◽  
Michel Kieffer ◽  
Hélène Piet-Lahanier
Keyword(s):  
Target Localization ◽  
Bounded Error ◽  
Localization And Tracking

Intelligent Tracking and Positioning of Targets Using Passive Sensing Systems

2019 ◽  
pp. 286-305
Author(s):  
Saad Iqbal ◽  
Usman Iqbal ◽  
Syed Ali Hassan
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
System Performance ◽  
Target Localization ◽  
The Other ◽  
Conventional System ◽  
Sensing Systems ◽  
Passive Sensing ◽  
Indoor Scenarios ◽  
Localization And Tracking

Target localization and tracking has always been a hot topic in all eras of communication studies. Conventional system used radars for the purpose of locating and/or tracking an object using the classical methods of signal processing. Radars are generally classified as active and passive, where the former uses both transmitter and receivers simultaneously to perform the localization task. On the other hand, passive radars use existing illuminators of opportunity such as wi-fi or GSM signals to perform the aforementioned tasks. Although they perform detection using classical correlation methods and CFAR, recently machine learning has been used in various application of passive sensing to elevate the system performance. The latest developed models for intelligent RF passive sensing system for both outdoor and indoor scenarios are discussed in this chapter, which will give insight to the readers about their designing.

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