Navigation using VLF signals with artificial neural networks

Curro, Joseph; Raquet, John; Borghetti, Brett J.

doi:10.1002/navi.264

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…GPS is especially susceptible to interference during solar storms. Establishing radio navigation based on very low frequency (VLF) radio signals focuses on the possibility of positioning, navigating, and timing using VLF signals in GPS-degraded or unavailable conditions (Curro et al 2018). VLF radio frequency range 3-30 kHz is widely utilized for navigation, timing (Niu et al 2009) earthquake prediction (Singh et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Observation and Analysis of VLF Nocturnal Multimode Interference Phenomenon based on Waveguide Mode Theory

Yang,

Niu,

Wang

et al. 2023

Res. Astron. Astrophys.

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Very low frequency (VLF) signals are propagated between the ground-ionosphere. Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night, leading to abnormalities in the received VLF signal. This study uses the VLF signal received in Qingdao City, Shandong Province, from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation. The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal. However, the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares, therefore the two phenomena are studied in this work. It is concluded that the X-ray will not affect the phase of VLF signals at night, but the energetic particles will affect the phase change, and the influence of energetic particles should be excluded in the study of multimode interference phenomena. Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for very low frequency navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.

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Section: Introductionmentioning

confidence: 99%

Observation and Analysis of VLF Nocturnal Multimode Interference Phenomenon based on Waveguide Mode Theory

Yang,

Niu,

Wang

et al. 2023

Res. Astron. Astrophys.

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“…There are tasks that do not require large-scale neural architectures and that can be effectively performed by a class of simpler traditional neural networks, having up to five hundred neurons [1] which, in the context of this paper, are called small/mid-scale networks. Low/high-level robot control [2][3][4][5][6][7], prediction of complex object behaviour [8][9][10], and robot navigation [11][12][13] are examples of tasks that are well-suited for this class of networks.…”

Section: Introductionmentioning

confidence: 99%

Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems

Praczyk

2022

Electronics

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The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals.

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“…Classification of the collected pressure signals is the basis of gait recognition. Common classification algorithms include support vector machine (SVM) [ 9 , 10 , 11 ], artificial neural networks (ANN) [ 12 , 13 , 14 ], and random forests (RF) [ 15 , 16 , 17 ]. SVM is a binary classification model, which constructs a hyperplane with the largest geometric distance and maps it into a high-dimensional space to classify specific objects.…”

Section: Introductionmentioning

confidence: 99%

Plantar Pressure Detection System Based on Flexible Hydrogel Sensor Array and WT-RF

Liu

Xiao

Wang

et al. 2021

Sensors

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This paper presents a hydrogel-based flexible sensor array to detect plantar pressure distribution and recognize the gait patterns to assist those who suffer from gait disorders to rehabilitate better. The traditional pressure detection array is composed of rigid metal sensors, which have poor biocompatibility and expensive manufacturing costs. To solve the above problems, we have designed and fabricated a novel flexible sensor array based on AAM/NaCl (Acrylamide/Sodium chloride) hydrogel and PI (Polyimide) membrane. The proposed array exhibits excellent structural flexibility (209 KPa) and high sensitivity (12.3 mV·N−1), which allows it to be in full contact with the sole of the foot to collect pressure signals accurately. The Wavelet Transform-Random Forest (WT-RF) algorithm is introduced to recognize the gaits based on the plantar pressure signals. Wavelet transform realizes the signal filtering and normalization, and random forest is responsible for the classification of the processed signals. The classification accuracy of the WT-RF algorithm reaches 91.9%, which ensures the precise recognition of gaits.

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Navigation using VLF signals with artificial neural networks

Cited by 3 publications

References 44 publications

Observation and Analysis of VLF Nocturnal Multimode Interference Phenomenon based on Waveguide Mode Theory

Observation and Analysis of VLF Nocturnal Multimode Interference Phenomenon based on Waveguide Mode Theory

Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems

Plantar Pressure Detection System Based on Flexible Hydrogel Sensor Array and WT-RF

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