Passive sonar target localization and tracking using sequential bayesian filter in uncertain sea environment

2012 ◽  
Vol 131 (4) ◽  
pp. 3313-3313
Author(s):  
Hangfang Zhao ◽  
Xianyi Gong ◽  
Zibin Yu
Keyword(s):  
Target Localization ◽  
Bayesian Filter ◽  
Passive Sonar ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document