dedicated smart ir barrier for obstacle detection in railways j. jesús garcía, cristina losada,...

Dedicated Smart IR Barrier for Obstacle Detection in Railways

J. Jesús García, Cristina Losada, Felipe Espinosa, Jesús Ureña, Álvaro Hernández, Manuel Mazo, Carlos de Marziani, Ana Jiménez,

Emilio Bueno, Fernando Álvarez,

Electronics Department.

University of Alcalá. Spain.IECON’05

IECON 2005, Raleigh, North Carolina, November 6-10 2005 2

Index

Introduction Sensorial system Emission codification False alarms Obstacle location Conclusions


Introduction (I)

Infrared barriers on railway applications Goals:

To detect the presence of obstacles avoiding false alarms To know the obstacle position inside the area of interest

Special environments conditions Adverse weather Low SNR

System keys Codification of Information:

Mutually Orthogonal Complementary Sets of Sequences –MOCSS- Geometrical distribution of emitters and receivers


Introduction (II)

High speed lines: overpasses and tunnels

Overpass Tunnel

Standard rail lines: level crossings

CRITICAL POINTS FOR OBSTACLES ON RAILWAYS


Sensorial System (I)

Structure of the Smart IR barrier

Reception System

Emission System Control System

Alarms

Other sensorial systems

Weather conditions

Data Fusion

Vital zone

Non Vital zone

Non Vital zone


Sensorial System (II)

Requirements

Detection of obstacles without false alarms

Discrimination, at least, between vital area and non vital one

Minimum obstacle size: 0.5x0.5x0.5 m (rail regulations)

Distance between emitters: 25 cm (also between receivers)

Distance between emitter and receiver barriers: >14 m


Sensorial System (III)

Classical solution with IR barrier

Emitter and receiver are aligned in the axial axis. This allows to detect the presence of obstacles between both elements

Impossible to know if the obstacle is located or not in the railway vital area


Sensorial System (IV)

Our proposal: Every receiver processes the information from three emiters (multi-emission) Specific geometrical distribution for the infrared barrier


Index

Abstract Sensorial system Emission codification False alarms Obstacle location Conclusions


Emission Codification (I)

PREVIOUS NOTES

The emission is carried out in a continuous way . The obstacle appearence generates a lack of the reception.

Every receiver detects the radiation from three emiters. How are they distinguished ?

Mutually Orthogonal Complementary Sets of Sequences (MO CSS) are used in order to:

Identify the emissions, and Avoid emitters interferences.


Emission codification (II)

Mutually Orthogonal Complementary Sets of Sequences

4 Sets are generated (number of sets is power of 2 to be MO), but only three are used.

Every set includes 4 complementary binary sequences {-1,+1}, {a, b, c, d} of length L,

Given a set i {ai, bi, ci, di}, if it is CSS then meets : The addition of their auto-correlation function is ideal ([k])

Given 2 sets, if they are MO then meets They both have the same length The addition of their cross-correlation function is zero


Emission codification (III)

Detector output for every emission. (L=256, SNR=-6dB)

a b c d1 1 1 1

Em itte r 2

Em itte r 3

Em itte r 4

Re c e ive r

Em itte r 1

a b c d2 2 2 2

a b c d3 3 3 3

a b c d4 4 4 4

Sync hro n iza tio n

Every emitter i transmits the set {aij, bij, cij, dij} being j=1…L, continously.To emit every symbol we use interleaving.

Every receiver detects a periodic signal with period 4L and a peak 4L.

j

jii LjkLkzoutputDetector )4(4_

0 512 1024 1536 2048 2560 3072 3584 4096-10

010

256 length sequences. SNR = -6dBs

Rec

eive

dsi

gnal

0 512 1024 1536 2048 2560 3072 3584 4096

0500

1000

Set

1

0 512 1024 1536 2048 2560 3072 3584 4096

0500

1000

Set

2

0 512 1024 1536 2048 2560 3072 3584 4096

0500

1000

Set

3

0 512 1024 1536 2048 2560 3072 3584 4096

0500

1000

Set

4


Emission codification (IV)

Emission detection in a practical case (FPGA)

Emitted signal

Peak detection

T

T

TT

1) Ch 1: 5 Volt 5 ms 2) Ch 2: 5 Volt 5 ms

Without obstacle

T

T

TT

1) Ch 1: 5 Volt 5 ms 2) Ch 2: 5 Volt 5 ms

With obstacle

4L


Index



False alarms (I)

False alarm Alarm:

the receiver does not detect emissions during a predefined time (temporary lack of information)

False alarm: the control system generates an alarm but there is not

obstacle

Examples of false alarms Solar radiation: photodiode saturation Atmospheric attenuation (fog, rain, dust, …..)


False alarms (II)

Atmospheric attenuation (dB), due to meteorology, it can be calculated in function of visibility V(Km) and link range R(Km):

RV

dBLq

atm

55017)(

Visibility V Weather condition q Expected attenuation

V>50km Very clear 1.6 0.19 dB/km

6km<V<50km Clear 1.3 1.82-0.48 dB/km

1km<V<6km Haze /snow /light rain 0.585·V1/3 13.2-1.82 dB/km

0.5km<V<1km Light fog /snow / heavy rain 0.585·V1/3 27.82-13.2 dB/km

V<0.5km Thick fog 0.585·V1/3 >27.82 dB/km


False alarms (III)

How to avoid false alarms

Due to atmospheric attenuation, the correlator output degrades.

To avoid false alarms, an adaptive threshold for the peak detection is proposed, The atmospheric degradation is estimated by polynomial interpolation of degree 1, The estimator output is used to adapt dynamically the threshold.

PeakD etecto r

R eceiver j

O u tdoo r C hann el

z [k] J

Em itte r 1{a ,b ,c ,d }1 1 1 1

Em itte r 2{a ,b ,c ,d }2 2 2 2

Em itte r 3{a ,b ,c ,d }3 3 3 3

Em itte r 4{a ,b ,c ,d }4 4 4 4

A tm o sp h e ric attenu atio nSo lar in terferences

C orrelato rr[k] +

[k]{a ,b ,c ,d }j j j j

Estim atedO utp ut

z [k] J


False alarms (IV)

False alarms discrimination results

0 500 1000 1500 2000 -1000

0 1000

Interpolating Polynomial of degree 1 with SNR = 0dBs Window size 40 samples

Very Clear Light fog Thick fog Haze Very clear

Correlation output

Prediction Threshold

0 500 1000 1500 2000 0 0.2 0.4 0.6 0.8 1 Obstacle detection with dynamic threshold

0 500 1000 1500 2000 0 0.2 0.4 0.6 0.8 1 Obstacle detection with fixed threshold

True Obstacles

Obstacle detection

Incorrect obstacle detection

Dynamic threshold evaluation in different weather conditions.

0 100 200 300 400 500 600 700

0

50

100

150

200Low solar radiation High solar radiation

0 100 200 300 400 500 600 700

0

0.2

0.4

0.6

0.8

1

Dynamic Threshold

0 100 200 300 400 500 600 700

0

0.2

0.4

0.6

0.8

1

Fixed Threshold

Dynamic threshold evaluation with different relative levels of sunlight


Index



Obstacle location (I)

Once the obstacle is detected, its location is possible according to the sensor geometrical distribution

3 boolean functions allows one to recognize 3 different zones

0 0.5 1 1.5 2 2.5 0 2 4 6 8

10 12 14

Track 1

Track 2

Zone 1

Zone 2

Zone 3


Obstacle location (II)

Detection in the track area Detection in the non vital area

If the obstacle is bigger than 0.5 m, at least 6 beams are not received


Conclusions

A smart system for obstacle detection on railways is proposed, based on IR barriers.

The system detects the obstacle, and locates it inside the region of interest (track or non vital area).

The codification technique based on MO CSS allows to identify the emissions and mitigate interferences among emitters.

Typical false alarms are analysed, and a solution is proposed to avoid them using a dynamic adaptive threshold.

Future work: The integration of filtering algorithms (KF, min-max, etc) to reduce the

effect of noisy sources.

ACKNOWLEDGEMENTS

MCYT: SILPAR and PARMEI projects

UAH: ISUAP project

Thank you very much!!

J. Jesús García, Cristina Losada, Felipe Espinosa, Jesús Ureña, Álvaro Hernández, Manuel Mazo, Carlos de Marziani, Ana Jiménez, Emilio

Bueno, Fernando Álvarez,

IECON’05

dedicated smart ir barrier for obstacle detection in railways j. jesús garcía, cristina losada,...

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