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Related Experiment Video

Updated: Dec 20, 2025

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Tracking by Risky Particle Filtering over Sensor Networks.

Jaechan Lim^1,2, Hyung-Min Park¹

¹Department of Electronic Engineering, Sogang University, Seoul 04107, Korea.

Sensors (Basel, Switzerland)

|June 4, 2020

Summary

This summary is machine-generated.

This study introduces minimax particle filtering (PF) to enhance target tracking in wireless sensor networks using received signal strength (RSS) data. The novel approach improves tracking accuracy and overcomes common particle filtering issues.

Keywords:

degeneracy minimax particle filtering received signal strength risk target tracking wireless sensor networks.

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Area of Science:

Wireless Sensor Networks
Signal Processing
Target Tracking

Background:

Wireless sensor networks (WSNs) are crucial for Internet of Things (IoT) applications.
Accurate target positioning is a significant challenge in WSNs.
Received Signal Strength (RSS) measurements are commonly used for localization.

Purpose of the Study:

To propose a minimax particle filtering (PF) method for improved target tracking in WSNs.
To address the degeneracy problem and reduce weight variance in generic PF algorithms.
To enhance tracking performance beyond asymptotic optimality.

Main Methods:

Developed a minimax particle filtering (PF) algorithm utilizing multiple RSS measurements.
Implemented a maximum risk criterion for particle weight computation.
Validated the approach across various PF variants including standard PF (SPF), auxiliary-PF (APF), regularized-PF (RPF), Kullback-Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF).

Main Results:

The minimax PF demonstrated decreased particle weight variance.
The proposed method showed immunity against the degeneracy problem inherent in generic PF.
Improved tracking performance was achieved compared to traditional PF methods.

Conclusions:

Minimax PF offers a robust solution for target tracking in WSNs.
The method enhances tracking accuracy and stability, particularly in challenging environments.
This probabilistic approach provides superior performance for WSN target localization.