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Field Application of Global Positioning System01:28

Field Application of Global Positioning System

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Errors in Global Positioning System01:26

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Introduction to Global Positioning System01:30

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The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
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Types of Global Positioning System Surveys01:30

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Inertial Frames of Reference01:03

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Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges.

Swapnil Sayan Saha1, Yayun Du2, Sandeep Singh Sandha3

  • 1University of California, Los Angeles, Los Angeles, CA, USA.

IEEE/ION Position Location and Navigation Symposium : [Proceedings]. IEEE/ION Position Location and Navigation Symposium
|September 22, 2023
PubMed
Summary
This summary is machine-generated.

This study presents AI-enhanced inertial navigation for resource-constrained Internet-of-Things (IoT) platforms. It introduces lightweight, physics-informed models for accurate localization in GPS-denied environments, overcoming limitations of traditional methods.

Keywords:
BayesianTinyMLdead-reckoninginertialkalman filteringneural architecture searchneural networksneurosymbolicodometryplatform-awaresequence learning

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Area of Science:

  • Robotics and Embedded Systems
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Inertial navigation offers low-cost localization for Internet-of-Things (IoT) devices in GPS-denied areas.
  • Traditional methods struggle with environmental complexities and drift.
  • AI-based approaches show promise but are resource-intensive and lack physical guarantees.

Purpose of the Study:

  • To explore methods for deploying real-time AI-enhanced inertial navigation on IoT platforms.
  • To develop lightweight, efficient, and accurate AI models for inertial odometry.
  • To address challenges in domain adaptation and data efficiency for AI-driven navigation.

Main Methods:

  • Platform-aware neural architecture search for ultra-lightweight deep neural-inertial odometry models.
  • Neurosymbolic AI techniques to create physics-informed and interpretable navigation models.
  • Efficient data collection and labeling strategies for domain-specific model fine-tuning.

Main Results:

  • Developed neural-inertial odometry models 31-134× smaller than state-of-the-art AI methods, with comparable or superior localization resolution.
  • Generated models applicable to diverse platforms including humans, animals, underwater sensors, aerial vehicles, and robots.
  • Demonstrated effective domain adaptation using minimal labeled data (1 minute).

Conclusions:

  • AI-enhanced inertial navigation can be effectively deployed on resource-constrained IoT platforms.
  • Lightweight, neurosymbolic, and domain-adaptive models offer a viable path for robust localization.
  • Further research is needed to address open challenges in real-world AI-driven navigation systems.