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Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...
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Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation.

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  • 1Robotics Institute, Ningbo University of Technology, Ningbo 315048, China.

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Summary
This summary is machine-generated.

A novel positioning algorithm enhances accuracy using maximum likelihood estimation and semi-definite programming for improved target location. This method outperforms existing algorithms in simulations and experiments.

Keywords:
maximum likelihood estimationreceived signal strengthsemi-definite programmingtarget localizationwireless sensor networks

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Area of Science:

  • Wireless communication
  • Signal processing
  • Optimization algorithms

Background:

  • Accurate target positioning is crucial in various applications.
  • Existing Received Signal Strength (RSS) based algorithms face accuracy limitations.
  • Robust algorithms are needed for both known and unknown noise levels (L0).

Purpose of the Study:

  • To propose a new positioning algorithm utilizing RSS measurements.
  • To enhance positioning accuracy by addressing non-convex optimization problems.
  • To extend the algorithm's applicability to scenarios with unknown noise characteristics.

Main Methods:

  • Employing maximum likelihood estimation (MLE) to model the positioning problem.
  • Transforming the non-convex RSS model into a convex one using semi-definite programming (SDP).
  • Developing a solution for both L0-known and L0-unknown positioning scenarios.

Main Results:

  • The proposed algorithm successfully transforms a non-convex problem into a convex one.
  • Achieved global minimum for target location estimation.
  • Demonstrated superior accuracy compared to existing positioning algorithms in simulations and experiments.

Conclusions:

  • The integration of MLE and SDP provides a robust framework for RSS-based positioning.
  • The algorithm offers enhanced accuracy and broader applicability, particularly for L0-unknown conditions.
  • This approach represents a significant improvement over current positioning techniques.