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Distance estimation using RSSI and particle filter.

Janja Svečko1, Marko Malajner2, Dušan Gleich2

  • 1Margento R&D, Gosposvetska 84, Maribor, Slovenia.

ISA Transactions
|December 3, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a particle filter for accurate radio frequency distance estimation using multiple antennas and Received Signal Strength Indicator (RSSI). The method enhances positioning accuracy in wireless systems.

Keywords:
Bayesian estimationMultiple antennasParticle filterRSSIRecursive estimationWSN

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Area of Science:

  • Wireless communication
  • Signal processing
  • Estimation theory

Background:

  • Accurate distance estimation is crucial for localization in wireless networks.
  • Received Signal Strength Indicator (RSSI) is a common metric for ranging, but prone to noise and multipath effects.
  • Particle filters offer a robust framework for state estimation in non-linear and non-Gaussian systems.

Purpose of the Study:

  • To develop and evaluate a particle filter algorithm for precise distance estimation.
  • To investigate the impact of antenna placement (parallel and circular) on estimation accuracy.
  • To utilize RSSI measurements within a Bayesian inference framework.

Main Methods:

  • Implementation of a particle filter algorithm to estimate distance as a hidden state.
  • Utilizing Received Signal Strength Indicator (RSSI) from radio frequency signals.
  • Employing two antenna configurations: parallel and circular.
  • Modeling prior distributions using log-normal and ground reflection propagation models.
  • Leveraging IEEE 802.15.4 physical layer for communication.

Main Results:

  • The particle filter effectively estimated distance using RSSI with multiple antennas.
  • Antenna placement influenced the performance of the distance estimation algorithm.
  • The algorithm demonstrated robustness in handling signal variations through weighted particle updates and resampling.

Conclusions:

  • Particle filters provide a viable and effective method for RSSI-based distance estimation.
  • The proposed algorithm offers improved accuracy for localization applications.
  • Further research can explore advanced propagation models and antenna configurations.