Real-time target localization and tracking by N-ocular stereo

2002 ◽  
Author(s):  
T. Sogo ◽  
H. Ishiguro ◽  
M.M. Trivedi
Keyword(s):  
Real Time ◽  
Target Localization ◽  
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

An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

2021 ◽  
pp. 101-107
Author(s):  
Mohammad Alshehri ◽  
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Initial Position ◽  
Swarm Optimization ◽  
The Real ◽  
Assisted Navigation ◽  
Glowworm Swarm Optimization ◽  
Tracking Process ◽  
Wide Range ◽  
Localization And Tracking

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.

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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