1. Intrusion Detection In Open Field
Using Geophone
Presented By
Nuthan Prasad KB
50027229
Robotics Engineering, 4th Semester, MTech
UPES, Dehradun
Under the Guidance of
Rajesh Singh
Assistant Professor, Department of Electronics
and Instrumentation Engineering
UPES, Dehradun

2. Problem Statement
• Providing security in an open field like border between two country, forest area is very
difficult using cameras as the area for which security is provided will be very large. As
the area increases, number of cameras used will also increase and monitoring such
large number of cameras will be come practically impossible.
• However, Sensors like infrared sensor, temperature sensors which can be used as
alternative for cameras. But range of such sensors are very less to use it for providing
security in open field.
• Therefore, better way of sensing intrusion is by sensing the seismic vibrations or
waves generated when any intrusion takes place in an large open field.

3. Introduction - Seismic Wave
• They are waves of energy that travel through and across the earth surface
• They are result of earth quake, explosion, volcano, anthropogenic sources
• They are measured using seismometer, hydrophone, accelerometer, geophones
Seismic
Waves
Body
Waves
Primary
Waves
Secondary
Waves
Surface
Waves
Rayleigh
Waves
Love
Waves
• Rayleigh wave attenuates at lesser rate comparatively and travels longer distance
• Rayleigh wave has bulk of the energy of seismic wave
• Therefore, Geophone is used to sense Rayleigh wave

4. Introduction - Geophone
• Geo Sensor is a magnet hanging through the spring with electromagnetic coils
around to produce voltage variations
• Geo Sensor senses the propagating seismic waves along the axis (Axis of Rayleigh)
in which it is placed
• Produces voltage variations according to the variation of propagating wave
intensity

5. Previous Work
Classification Of Footsteps From Sensor Data
• Footstep Detection and Tracking by George Succi, Daniel Clapp, Robert Gampert,
Gervasio Prado
• Seismic Footstep Signal Characterization, A. Pakhomov, A. Sicignano, M. Sandy, and
T. Goldburt
• Seismic Signals and Noise Assessment for Foot Step Detection Range Estimation in
Different Environments, Alex Pakhomov and Tim Goldburt
Methodology to classification footstep seismic signal from non footstep seismic signal
using anti aliasing filter, sampler(ADC), band pass filter, envelope detector, kurtosis
block is explained in above papers. Sensor node in this project is developed based on
above papers.

6. Objective
The goal of the project is to develop Intrusion Detecting System using Geo sensors. In
order to accomplish this, following has to developed.
• Sensor Node
• Control Room and Unmanned Vehicle to navigate between intrusion site and
control room to take necessary action against intrusion.
• A Wireless Communication System to communicate between sensor node and
control room and vice versa.
Control
Room
Node 1
Node 3
Node 4
Node 2
Alerting (Buzzer and
Display Device showing the
location of intruder)
Unmanned
Vehicle

7. Sensor Node

8. Amplification
• Signal from geophone is in mv range.
• For easy processing of geo sensor signal , it has to be amplified in order to get the
signal in volt range.
• Gain = 5 v/5 mv = 1000
• Normal opamps does not provide gain of 1000. Therefore, a low power low voltage
instrumentation amplifier called AD620 is used.

9. Band Pass Filter
• The frequency range, which contains the main part of the footstep signal energy for a
distance greater than 6m, is between 10 Hz and 100 Hz. Therefore, Geophone signal is
passes through band pass filter to filter out all the frequency other then the frequency
between 10 Hz and 100 Hz

10. Envelope detected signal
• Output of band pass filter will have both positive and negative shoots and its frequency is
band limited between 10 Hz and 100 Hz.
• Performing kurtosis operation will be confusing for microcontroller, when whole of the signal
from band pass signal is considered, which leads to wrong calculation of kurtosis.
• This constraint, is overcome by subjecting the band limited signal to envelope detection to
track only the positive peak amplitudes before subjecting the signal to kurtosis operation.

11. Kurtosis and Communication
• Kurtosis is the measure of instant raise in the amplitude.
• Kurtosis is performed by microcontroller board.
• Microcontroller reads the signal through analog to digital converter at 1000 Hz
sampling frequency. i.e., Envelope signal is read every 1 ms through ADC and
stored in EEPROM of microcontroller using the concept of timer interrupts.
• Meanwhile, Simultaneously microcontroller perform kurtosis operation on
EEPROM data.
• Kurtosis is performed on 200ms of EEPROM data at once. i.e., 1000 samples per
second/ 5 steps per second = 200 samples
• If kurtosis>3, Intrusion is detected and informed to control room and Unmanned
Vehicle wirelessly along with its location information.
• Arduino Mega 2650 microcontroller, Global Positioning System and 433 MHz
Transmitter are used.

12. Main Function Flowchart of Sensor
Node

13. Interrupt Subroutine Flowchart of
Sensor Node

14. Control Room and Unmanned Vehicle
• Control Room has to continuously monitor for intrusion information from Sensor
Nodes.
• After finding intrusion and location information from sensor nodes wirelessly,
Control room has to send a Unmanned Vehicle to take action against intrusion.
• Unmanned Vehicle navigates to the sensor node using Global Positioning System to
take action against intrusion.
• In the project, only one node is developed. Hence control room is bypassed. Sensor
node informs intrusion details to UGV directly. Information is transmitted wirelessly
using 433 MHz transmitter. UGV receives the intrusion information wirelessly using
433 MHz receiver.

15. Flowchart of UGV

16. Wireless Communication System
• Depending on the range, area of field of security, power constraints, environment
different wireless communication can be chosen.
• Wireless communication takes between sensor nodes, control rooms and UGVs.
Communication could take place between two sensor nodes or between two
control rooms could as well.
• Communication could be simplex or duplex. All these factors depend on the design
of wireless communication networks design.
• However, in this project 433 MHz transmitter and receiver is used.

17. Result
Geophone available with us is not sensitive enough to capture the seismic signals
generated by human footstep.
Hence, the project is demonstrated using Signal Generator and DAC in Arduino Due
microcontroller board.

18. Conclusion and Future Work
This project has huge scope for updating. Following are few updates that can be
considered as updates.
• The Band pass filter used for filtering of footstep is fixed for this project. The filter
has to be made adaptive so that the filter can be modified based on the site and
incoming data.
• Envelope Detection, Amplification processes can be made adaptive.
• Unmanned All-Terrain vehicle, Quad copter can be used instead of simple UGV with
better technologies to navigate along with functionalities like obstacle avoidance,
transmitting video wirelessly and better algorithm to reach destination or intrusion
detected sensor node with shortest distance from control room.
• Range of wireless transmission can be increased using better wireless technologies
like Wi-Fi, Zig bee, etc.
Results demonstrated by generating the signal similar to the geophone signals using Signal
Generator and DAC of Arduino Due microcontroller board which hold true with results that can
be produced using geophone with sufficient sensitivity too.

19. References
• Footstep Detection and Tracking, George Succi, Daniel Clapp, Robert Gampert,
Gervasio Prado.
• Problems in Seismic Detection and Tracking ,Dr. George Succi, Dr.Gervasio Prado,
Robert Gampert, Torstein Pedersen and Hardave Dhaliwal
• Seismic Footstep Signal Characterization, A. Pakhomov, A. Sicignano, M. Sandy, and
T. Goldburt
• Seismic Signals and Noise Assessment for Foot Step Detection Range Estimation in
Different Environments, Alex Pakhomov and Tim Goldburt
• Personnel tracking using seismic sensors, Michael S. Richman, Douglas S. Deadrick,
Robert J. Nation, Scott L. Whitney
• Demonstration System for a Low-Power Seismic Detector and Classifier, Elliot
Richard Ranger , Master’s thesis ,MIT 2003

20. Thank You