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Related Experiment Video

Updated: Jan 16, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Research on the Localization Method of Outdoor Ground Vibration Signals Based on MEMS Accelerometers.

Runping Liu¹, Xiuyan Zhao¹, Bin Zhou²

¹School of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This study introduces a new method for pinpointing outdoor vibration sources using underground sensors. It achieves high accuracy in localization, crucial for intrusion detection in sensitive areas.

Keywords:

MEMS accelerometer TDOA localization ground vibration signal localization algorithm optimization algorithm outdoor localization time delay estimation algorithm

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Last Updated: Jan 16, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

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Published on: April 13, 2016

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

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Published on: February 4, 2018

Using Micro-Electro-Mechanical Systems MEMS to Develop Diagnostic Tools

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Published on: October 1, 2007

Area of Science:

Geophysics
Sensor Technology
Signal Processing

Background:

Critical areas require robust intrusion detection and precise localization capabilities.
Existing methods for outdoor vibration source localization face challenges in accuracy and environmental adaptability.

Purpose of the Study:

To develop and validate a high-precision outdoor ground vibration source localization method.
To enhance intrusion detection in critical areas using subterranean MEMS accelerometers.

Main Methods:

Optimized sensor array configuration using Particle Swarm Optimization (PSO) to minimize Geometric Dilution of Precision (GDOP).
Proposed a novel Sliding Window Derivative (SWD) algorithm for accurate time delay estimation of vibration signals.
Employed STA/LTA-AIC and an improved two-step weighted least squares method based on Time Difference of Arrival (TDOA) for source positioning.

Main Results:

Achieved an average positional error of 0.095 meters.
Attained an average directional error of 0.935 degrees.
Demonstrated the method's efficacy in high-precision localization in outdoor environments.

Conclusions:

The developed method effectively localizes outdoor ground vibration sources with high accuracy.
Subterranean-deployed MEMS accelerometers combined with novel signal processing techniques offer a viable solution for intrusion detection.