A Vessel’s Dead Reckoning Position Estimation by Using of Neural Networks

2018 ◽  
pp. 493-502
Author(s):  
Victor V. Deryabin ◽  
Anatoly E. Sazonov
Keyword(s):  
Neural Networks ◽  
Dead Reckoning ◽  
Position Estimation

scholarly journals Sensor Modeling for Underwater Localization Using a Particle Filter

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1549
Author(s):  
Humberto Martínez-Barberá ◽  
Pablo Bernal-Polo ◽  
David Herrero-Pérez
Keyword(s):  
Sequential Monte Carlo ◽  
Sensory Information ◽  
Dead Reckoning ◽  
Underwater Vehicle ◽  
Position Estimation ◽  
Sources Of Uncertainty ◽  
Sensor Signals ◽  
Underwater Navigation ◽  
Underwater Localization ◽  
Sensor Modeling

This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.

scholarly journals Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 397
Author(s):  
Hossein Shoushtari ◽  
Thomas Willemsen ◽  
Harald Sternberg
Keyword(s):  
Mobile Networks ◽  
Dead Reckoning ◽  
Simulated Data ◽  
Minimum Cost ◽  
Cost Effective ◽  
Satellite System ◽  
Position Estimation ◽  
Robust Solution ◽  
Position Data ◽  
Global Navigation Satellite

There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.

scholarly journals Ensemble of neural networks for 3D position estimation in monolithic PET detectors

2019 ◽  
Vol 64 (19) ◽  
pp. 195010 ◽  
Author(s):  
A Iborra ◽  
A J González ◽  
A González-Montoro ◽  
A Bousse ◽  
D Visvikis
Keyword(s):  
Neural Networks ◽  
Position Estimation ◽  
Pet Detectors

Simultaneous Identification and Localization of Still and Mobile Speakers Based on Binaural Robot Audition

2017 ◽  
Vol 29 (1) ◽  
pp. 59-71 ◽  
Author(s):  
Karim Youssef ◽  
◽  
Katsutoshi Itoyama ◽  
Kazuyoshi Yoshii
Keyword(s):  
Neural Networks ◽  
Speaker Identification ◽  
Position Estimation ◽  
Acoustic Features ◽  
Front End ◽  
Robot Audition ◽  
Speaker Tracking ◽  
Common Signal ◽  
Interaural Level Differences ◽  
Simultaneous Identification

[abstFig src='/00290001/06.jpg' width='300' text='Efficient mobile speaker tracking' ] This paper jointly addresses the tasks of speaker identification and localization with binaural signals. The proposed system operates in noisy and echoic environments and involves limited computations. It demonstrates that a simultaneous identification and localization operation can benefit from a common signal processing front end for feature extraction. Moreover, a joint exploitation of the identity and position estimation outputs allows the outputs to limit each other’s errors. Equivalent rectangular bandwidth frequency cepstral coefficients (ERBFCC) and interaural level differences (ILD) are extracted. These acoustic features are respectively used for speaker identity and azimuth estimation through artificial neural networks (ANNs). The system was evaluated in simulated and real environments, with still and mobile speakers. Results demonstrate its ability to produce accurate estimations in the presence of noises and reflections. Moreover, the advantage of the binaural context over the monaural context for speaker identification is shown.

Interests and Limits of Machine Learning-Based Neural Networks for Rotor Position Estimation in EV Traction Drives

2018 ◽  
Vol 14 (5) ◽  
pp. 1942-1951 ◽  
Author(s):  
Wided Zine ◽  
Zaatar Makni ◽  
Eric Monmasson ◽  
Lahoucine Idkhajine ◽  
Bruno Condamin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Position Estimation ◽  
Rotor Position ◽  
Traction Drives ◽  
Rotor Position Estimation

scholarly journals State Estimation for Mobile Robot using Partially Observable Markov Decision Process

1996 ◽  
Vol 8 (3) ◽  
pp. 272-277
Author(s):  
Daehee Kang ◽  
◽  
Hideki Hashimoto ◽  
Fumio Harashima
Keyword(s):  
Mobile Robot ◽  
Markov Decision Process ◽  
Decision Process ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Estimation Errors ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable ◽  
Robot Position

Dead Reckoning has been commonly used for position estimation. However, this method has inherent problems, one of the biggest being it always cumulates estimation errors. In this paper, we propose a new method to estimate a current mobile robot state using Partially Observable Markov Decision Process (POMDP). POMDP generalizes the Markov Decision Process (MDP) framework to the case where the agent must make its decisions in partial ignorance of its current situation. Here, the robot state means the robot position or current subgoal at which the mobile robot is located. It is shown that we will be able to estimate the mobile robot state precisely and robustly, even if the environment is changed slightly, through a case study.

Incidence Position Estimation in a PET Detector Using a Discretized Positioning Circuit and Neural Networks

2007 ◽  
pp. 684-691 ◽  
Author(s):  
Fernando Mateo ◽  
Ramón José Aliaga ◽  
Jorge Daniel Martínez ◽  
José María Monzó ◽  
Rafael Gadea
Keyword(s):  
Neural Networks ◽  
Position Estimation
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