1. Lightning detection and localization using extended Kalman filter Ines Ben Saïd U2S(ENIT) SYS’COM Master SYS’COM Master 2008-2009 2008-2009 National Engeneering School of Tunis

2. 2 References 1 - 1 - T. G. Wood, Geo-location of individual lightning discharges using impul- sive vlf electromagnetic waveforms, Phd thesis, The department of electrical engineering and the committee on graduate studies of stanford university, De- cember 2004. 2 - 2 - S. A. Cummer, Lightning and ionospheric remote sensing using vlf/elf radio atmospherics, Phd thesis, The department of electrical engineering and the committee on graduate studies of stanford university, August 1997. 3 - 3 - R. E. Kalman, A new approach to linear filtering and prediction problems, Transaction of the ASME Journal of Basic Engineering, (pp. 35{45), March 1960.

3. 3 Lightning Detection Earth+ Ionosphere = waveguide [ELF-VLF] = [300Hz-30KHz] radio atmospherics (‘sferic’) = Waves that propagates in the ELF/VLF band with low attenuation (~3dB/1000Km) Lightning detection via VLF data analyses Receivers Transmitters

4. 4 VLF receiver at LSAMA HardwareSoftware

5. 5 VLF receiver schema Two data types: - narrow band - broad band A/D Converter

6. 6 Broad band signals transmitters sferic

7. 7 Sferic caracteristics  Much of the sferic energy lies in [5KHz-15KHz] band (Cummer 2004 )  Duration ~4ms: - ~ 1ms VLF impulse - ~ 3ms ELF slow tail - ~ 3ms ELF slow tail (Cummer 2004 )

8. 8 Sferics detection method Identification  Two successif instants must be separated with an delay >= 4ms (sferic duration)  Determinate the simultaneous instants for the N/S and E/W signals. Proposed procedure N = 60000 samples T e = 100KHz

9. 9 Lightning localization IMPACT Tow receivers are sufficient Precision depend on optimization method

10. 10 Lightning localization Arrival azimuth calculation

11. 11 Triangulation

12. 12 Results and limites IMPACT method tested by simulation Source [45°N 60°E] 1st receiver : Vieques 2nd receiver : Palmer

13. 13 Proposed method: Extended Kalman filter Observation State Interest : non linear optimization used in GPS localization State representation

14. 14 Algorithm InitialisationPredictionCorrection

15. 15 Simulation results Source P1 [45°N 60°E] 1st receiver A: Vieques 2nd receiver B: Palmer Estimated position

16. 16 Simulations results Real Source [45°N 60°E] 1st receiver A: Vieques 2nd receiver B: Palmer Azimuth error 1°; Time difference Error 0.1ms Estimated position Real source

17. 17 Real data

18. 18 Real data localization  Source localized in the triangle using the two methods  A difference of ~200Km between the two methods Alger Iso time difference Extended kalman filter Iso time difference Alger Researsh zone

19. 19 Conclusion and perspective Automatic method for sferic detection Localization using extended Kalman filter Introduction of signal dynamic in physics problems Test of other optimization methods