4G LTE Detection using Nemo Handy

for PFSR Support System

By

NOOR HAFIZAH BINTI ABDUL AZIZ

Antenna Research Centre (ARC)

Faculty of Electrical Engineering, UiTM

Muhammad Anwar Aminudin

Faculty of Electrical Engineering, UiTM 1

Introduction Conventional radar: • Transmitter and Receiver are collocated. • Transmit electromagnetic waves in the space. • Hits the target present in the radar coverage area. The waves scatter

back to the receiver for detection. • All information about the target can be determined.

Receiver Transmitter

RT RR β

Target

Transmitter / Receiver

R

Target

Monostatic Bistatic

2

Introduction Conventional radar: • Transmitter and Receiver are collocated. • Transmit electromagnetic waves in the space. • Hits the target present in the radar coverage area. The waves scatter

back to the receiver for detection. • All information about the target can be determined.

Receiver Transmitter

RT RR β

Target

Transmitter / Receiver

R

Target

Monostatic

Forward Scatter

3

• Illuminators of opportunity is considered as the transmitter (passive radar

system).

• The angle between the transmitted and reflected rays is a bistatic angle, β.

Introduction: Passive Radar

Illuminator of

opportunity

Reference

Receiver

RT

RR

Target

β

Direct path Reflected path

Target Echo

Receiver Passive

Bistatic Radar

FM Radio, WiFi,

DVB, DAB,

WiMAX, GSM,

Satellite, LTE

4

• Special mode of bistatic radar.

• Narrow region stretched along the baseline of the bistatic angle, 𝛽 near 180⁰.

• The conventional FSR system transmits its own radio waves which can easily be

demolished by an enemy.

Baseline

Dedicated Transmitter Receiver

RT RR

Direct path (leakage)

Target

Forward Scatter Radar (FSR)

β

When 𝛽 near 180⁰, target blocks (“shadows”) EM energy travelling from the transmitter to the receiver.

5

• This could be done by integrating the forward scatter geometry (𝛽 near 180⁰) into the

passive radar system.

• The radar system is unseen by any hostile threat but on the other hand, it could detect

others.

– Leakage/direct signal which is unwanted in conventional radar, is good for FSR due to

signal perturbation of target.

• Integrating the forward scattering radar (FSR) mode in passive radar. The system can

get benefit from:

the enhancement in radar cross section (RCS)

the simple receiver system, thus low cost

the modulation of the specific transmitter does not influence the FS processing

signal.

Baseline

Receiver

RT RR

Direct path (leakage)

Target

Passive Forward Scatter Radar (FSR)

β

LTE Tower Base Station

8

LTE Based Passive FS Radar Applications

Passive FSR for

Traffic Surveillance

Passive FSR

for Building

Protection

Passive FSR for

Theft Surveillance

Passive FSR for

Border Protection

Passive FSR for

Low Altitude Air

Target Protection

LTE eNB Transmitter

Drone Insects migration monitoring

9

Sura, Dungun (Terengganu) (4.728, 103.433)

Experiment Site

10

PASSIVE RADAR SYSTEM

4.728029, 103.433336

Teluk Kemang, Port Dickson (Negeri Sembilan) (2.448, 101.856)

Experiment Site

11

BASE STATION

PASSIVE RADAR SYSTEM

2.448141, 101.855917

Taman Suria, UiTM Shah Alam (Selangor) (3.074, 101.498)

Experiment Site

12

2.754381, 101.441070

LTE Passive FSR Hardware

Receiving antenna

BPF

Amplifier

ADC

Detector

LPF & HPF

LNA

13

LTE Signal Spectrum

20 MHz

2.63 GHz

4G LTE with Center Frequency 1.8 GHz 14

Dungun Port Dickson Taman Suria

Arrangement of human trajectory

15

NEMO Handy

Human Moving Target Trajectory

Passive Radar

𝑨

LTE

5 m

𝑩

Speed (km/h) Moving target

58

0 m

Time (sec)

Am

plit

ude (

volt)

16

17

PLACE PERSON WIDTH (m) HEIGHT (m) DIMENSION (m2)

DUNGUN 1 0.26 1.78 0.4628

2 0.29 1.76 0.5104

PORT

DICKSON

1 0.26 1.76 0.4576

2 0.28 1.48 0.4144

TAMAN

SURIA

1 0.32 1.76 0.5632

2 0.29 1.76 0.5104

The information of the persons for the targets

18

Number of Sample Experiments

Person Number of samples

Dungun Port Dickson Taman Suria

Training Testing Training Testing Training Testing

1 31 4 33 4 34 4

2 34 4 35 4 32 4

Total 65 8 68 8 66 8

73 76 74

Cla

ssific

ation

Blo

ck D

iagra

m

Normalisation

Principal Component

Analysis (PCA)

Training

Classification

Rules/ k-NN

Time Domain

Signature

Display Person Category

Power Spectrum Estimation

(Frequency Domain)

1

2

3

Fea

ture

Extr

acti

on

19

Extract Data

Classification

Data Recording

Select Data

PFSR NEMO HANDY

Time Domain Signature

20

Time Domain Signature

21

Frequency Domain Signature

22

Frequency Domain Signature

23

Principle Component Analysis

24

Principle Component Analysis

25

NEMO Analysis

26

Condition Parameters

RSRP (dB) RSRQ (dB)

Excellent >=-80 >=-10

Good -80 to -95 -10 to -15

Moderate -95 to -110 -15 to -20

Poor <-110 <-20

Confusion Matrix

The classification results obtained from Dungun, Port Dickson and Taman Suria were 75%, 100% and 100%, respectively.

Dungun Port Dickson

Taman Suria

27

Person No. of

samples

Automatically

classified (%)

Person

1 2

1 37 75 25

2 39 25 75

Person No. of

samples

Automatically

classified (%)

Person

1 2

1 38 100 0

2 36 0 100

Person No. of

samples

Automatically

classified (%)

Person

1 2

1 35 100 0

2 38 0 100

Conclusion LTE signal is very useful in this PFSR system to detect human’s Doppler for differentiate and

classify targets due to forward scatter main lobe (FSML) which the targets’ RCS are influenced only

by the size and the shape of their silhouettes.

Nemo Handy was used to detect the signal strength of LTE signal and support passive forward

scattering radar system in detecting moving humans in the range of forward scatter region.

A simple LTE based passive forward scattering radar receiver and system in the experimental setup

was implemented at different places which are Dungun, Port Dickson and Taman Suria, (UiTM) with

2 different persons as target.

Classification for the radar system can be clearly seen, and this result discovered that body build

and size do indeed affect the power signal received by the radar receiver.

The farther the distance, the higher the environment noise, and so, it will be tougher to classify the

signal.

Nemo Handy proved that the value of RSRP and RSRQ parameters can affect the classification of

human Doppler signature.

The best value for RSRP should be less than -70dB, and RSRQ should be less than -12dB to

support better detection results in passive forward scatter radar system.

This project was successful in detecting the signal strength of 4G LTE network that focused on

Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), which

support the passive forward scattering radar system. 28