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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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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
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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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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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In coordinate geometry, determining the central point between two locations is common. This central point, or midpoint, lies exactly halfway along the line segment connecting two points in a two-dimensional space. It has applications in mathematics, physics, engineering, and various planning disciplines.Given two points labeled as A (x1, y1) and B (x2, y2) on a coordinate plane, a straight line segment can be plotted between them. The midpoint, labeled point M, divides this segment into two...
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Received Signal Strength Indicator-Based Indoor Localization Using Distributed Set-Membership Filtering.

Bo Yang, Quanwei Qiu, Qing-Long Han

    IEEE Transactions on Cybernetics
    |April 20, 2020
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    Summary
    This summary is machine-generated.

    This study introduces a novel indoor localization method using received signal strength indicators (RSSI) and set-membership filtering. It accurately locates targets even with unknown noise, improving practical applications.

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    Area of Science:

    • Robotics
    • Signal Processing
    • Navigation Systems

    Background:

    • Existing localization methods often require prior knowledge of noise characteristics, limiting real-world applicability.
    • Indoor localization using Received Signal Strength Indicator (RSSI) is challenging due to noise and unknown environmental factors.
    • Set-membership filtering offers a robust approach for state estimation under bounded uncertainties.

    Purpose of the Study:

    • To develop an indoor localization system robust to unknown-but-bounded process and measurement noises.
    • To enhance the accuracy of Received Signal Strength Indicator (RSSI)-based localization.
    • To implement a distributed set-membership filtering approach for reliable target tracking.

    Main Methods:

    • Estimation of transmit power and path-loss exponent using a novel least-squares curve fitting (LSCF) method.
    • Development of a convex optimization method for state ellipsoid estimation under unknown-but-bounded noises.
    • Establishment of a recursive algorithm to compute a global ellipsoid for guaranteed target localization.

    Main Results:

    • The proposed LSCF method effectively estimates key RSSI-based localization parameters.
    • Convex optimization provides accurate state ellipsoid estimations despite bounded noise uncertainties.
    • The recursive algorithm successfully computes a global ellipsoid, ensuring accurate target localization at each time step.

    Conclusions:

    • The developed set-membership filtering method demonstrates significant accuracy and effectiveness for indoor localization.
    • This approach overcomes the limitations of traditional methods by not requiring prior statistical noise knowledge.
    • Experimental validation confirms the robustness and practical viability of the proposed localization technique.