1. 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

2. IECON 2005, Raleigh, North Carolina, November 6-10 20052 Index  Introduction  Sensorial system  Emission codification  False alarms  Obstacle location  Conclusions

3. IECON 2005, Raleigh, North Carolina, November 6-10 20053 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

4. IECON 2005, Raleigh, North Carolina, November 6-10 20054 Introduction (II)  High speed lines: overpasses and tunnels Overpass Tunnel  Standard rail lines: level crossings CRITICAL POINTS FOR OBSTACLES ON RAILWAYS

5. IECON 2005, Raleigh, North Carolina, November 6-10 20055 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

6. IECON 2005, Raleigh, North Carolina, November 6-10 20056 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

7. IECON 2005, Raleigh, North Carolina, November 6-10 20057 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

8. IECON 2005, Raleigh, North Carolina, November 6-10 20058 Sensorial System (IV)  Our proposal: Every receiver processes the information from three emiters (multi-emission) Specific geometrical distribution for the infrared barrier

9. IECON 2005, Raleigh, North Carolina, November 6-10 20059 Index  Abstract  Sensorial system  Emission codification  False alarms  Obstacle location  Conclusions

10. IECON 2005, Raleigh, North Carolina, November 6-10 200510 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.

11. IECON 2005, Raleigh, North Carolina, November 6-10 200511 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 {a i, b i, c i, d i }, 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

12. IECON 2005, Raleigh, North Carolina, November 6-10 200512 Emission codification (III) Detector output for every emission. (L=256, SNR=-6dB) Every emitter i transmits the set {a ij, b ij, c ij, d ij } 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.

13. IECON 2005, Raleigh, North Carolina, November 6-10 200513 Emission codification (IV)  Emission detection in a practical case (FPGA) Emitted signal Peak detection Without obstacle With obstacle 4L

14. IECON 2005, Raleigh, North Carolina, November 6-10 200514 Index  Abstract  Sensorial system  Emission codification  False alarms  Obstacle location  Conclusions

15. IECON 2005, Raleigh, North Carolina, November 6-10 200515 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, …..)

16. IECON 2005, Raleigh, North Carolina, November 6-10 200516 False alarms (II)  Atmospheric attenuation (dB), due to meteorology, it can be calculated in function of visibility V(Km) and link range R(Km): Visibility VWeather conditionqExpected attenuation V>50kmVery clear1.60.19 dB/km 6km<V<50kmClear1.31.82-0.48 dB/km 1km<V<6kmHaze /snow /light rain0.585·V 1/3 13.2-1.82 dB/km 0.5km<V<1kmLight fog /snow / heavy rain0.585·V 1/3 27.82-13.2 dB/km V<0.5kmThick fog0.585·V 1/3 >27.82 dB/km

17. IECON 2005, Raleigh, North Carolina, November 6-10 200517 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.

18. IECON 2005, Raleigh, North Carolina, November 6-10 200518 False alarms (IV)  False alarms discrimination results Dynamic threshold evaluation in different weather conditions. Dynamic threshold evaluation with different relative levels of sunlight

19. IECON 2005, Raleigh, North Carolina, November 6-10 200519 Index  Abstract  Sensorial system  Emission codification  False alarms  Obstacle location  Conclusions

20. IECON 2005, Raleigh, North Carolina, November 6-10 200520 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

21. IECON 2005, Raleigh, North Carolina, November 6-10 200521 Obstacle location (II) Detection in the track areaDetection in the non vital area If the obstacle is bigger than 0.5 m, at least 6 beams are not received

22. IECON 2005, Raleigh, North Carolina, November 6-10 200522 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

23. 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