Methods for buried target detection and localization using magnetic measurements

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1 UMR 6285 CID/PRASYS Methods for buried target detection and localization using magnetic measurements PRASYS team seminar Author : Romain Schwab Project : DGA RAPID BODAMM Date : 27/03/2018 1

2 Summary I. BODAMM project II. An introduction to underwater magnetometry III. Methods IV. Results 2

3 Team Context BODAMM system BODAMM PROJECT 3

4 BODAMM, a versatile team Buried Objects Detection by Acoustic and Magnetic Methods DGA RAPID Project Partners DGA : main funder RTsys : technical know-how in marine robotics Maree : seabed perception system ENSTA Bretagne : methods & programming 4

5 Context (1) Offshore windfarm Zentilia World offshore wind power capacity Wind power (MW) 2000lb WWII bomb in Bay of Brest French Navy Critical dangers even in assumed well-knowned area Marine energy world growth : +30% per year 5

6 Context (2) 1. UneXploded Ordnance (UXO) survey 2. Cable buried depth control cable UXO dredging Boskalis Sub-bottom imager (SBI) pangeosubsea Survey systems : towfish or ROV 6

7 BODAMM System (1) Magnetometer Sea bottom geoacoustic sensor AUV Sidescan 3knt 8m 3m UXO AUV : modern environment assessment solution for buried objects 7

8 BODAMM System (2) Hybrid moderate-cost solution for multiple targets localization Targets Energy subsea cable TenneT/archive old mine Shutterstock WWII UXO Spiegel Sensors Acoustic Magnetic Data fusion Detection rate False alarm rate 8

9 Sources Passive sensors A «noisy» environment Detection capability Le Donateur Cristina Franco INTRODUCTION TO MAGNETOMETRY 9

currents macroscopic B I cable Ferromagnetism

10 Magnetic field sources Moving electric charges Ferromagnetic materials (iron) atomic Electric currents macroscopic B I cable Ferromagnetism principle iron objects (UXOs, wrecks, ) subsea energy cables 10

of Brest 1 r 3 rate of decrease with distance r from the source

11 Magnetic field anomalies Magnetic field unit : Tesla (T) Typical anomaly : 1-100nT Ponctual target : Magnetic anomaly - survey in Bay of Brest 1 r 3 rate of decrease with distance r from the source 1000kg of iron : Range [m] 5m 10m 15m 20m Anomaly [nt] 120nT 15nT 4nT 2nT 11

12 A «noisy» environment 12

13 Magnetic sensors (passive) technology scalar (cesium) vectorial (flux-gate) measurement absolute relative information intensity intensity + direction sensitivity <0,1nT if mobile : >5nT bandwidth 20Hz 2000Hz price 25k 3k consumption 30W 3W Bartington Grad-13 : 13 sensing magnetometer reference magnetometer

14 Detection capability Detection threshold : 10nT 14

15 UXO-like target Localization Automatic detection METHODS 15

16 Global method UXO case (g x, g y, g z ) accelerometers raw data ( B x, B y, B z ) Pre-processing Today Automatic detection x y m x m y m z Localization (2) D t 0 Localization (1) N targets 3D position intensity Local speed computation 16

17 UXO-like target signal Magnetic dipole : B r = μ 0 4π r 5 B(r) 3 m r r r 2 m Z Y X magnetometer V=3 knots r 17 m UXO

18 Inverse problem dipolar model Model : B r = μ 0 4π r 5 z 2 m x + 3xzm z + 3xym y + 2x 2 y 2 m x z 2 m y + 3yzm z + 3xym x + 2y 2 x 2 m y 2z 2 m z + 3z xm x + ym y x 2 + y 2 m z Model parameters : position r = x y z intensity m = m x m y m z Non-linear inverse problem 6 parameters 18

Solution Sensor trajectory = straight line at

5/2 f 1 u uf 0 (u) f 2 u u 2 f 0 (u) D = x 2 + y 2

2x 2 y 2 m x 3Dxm z D 2 m x 3xym x + 2y 2 x 2 m y

0 (u) f 1 (u) f 2 (u) Diporal magnetic signal basis

19 Solution Sensor trajectory = straight line at constant speed Anderson functions : 1 f 0 u 1 + u 2 5/2 f 1 u uf 0 (u) f 2 u u 2 f 0 (u) D = x 2 + y 2 u = z(t) D = V (t t 0) D B r = μ 0 4πD 5 3xym y + 2x 2 y 2 m x 3Dxm z D 2 m x 3xym x + 2y 2 x 2 m y 3Dym z D 2 m y D 2 m z 3D(xm x + ym y ) 2D 2 m z f 0 (u) f 1 (u) f 2 (u) Diporal magnetic signal basis : Non-dipolar signal rejected 2 parameters only (D and t 0 ) 19

20 Projection onto Anderson basis Elementary signals shape : Dipolar target signal basis (t 0 =10s and D=5m) f 0 (u) f 1 (u) f 2 (u) Multi-channel approach for (D, t 0 ) likely values : 2D 20 t 0

21 Localization Matching pursuit D t 0 [1m, 2m, 3m, 4m, 5m] [42s, 43s, 44s, 45s, 46s, 47s] Multi-resolution execution time gain : 48s -> 1s 21

22 Automatic target detection Moving window + matched filtering Magnet at D=4m : B f 0 22

23 Lorient RESULTS 23

24 Lorient november 2017 (1) Detection of a mine-like target by acoustics and magnetics in very shallow water (2m) pole SCAMPI magnetometer low-freq. acoustic source GESMA mine-like target 110kg (60kg of iron) electric source ponton y 2m mine Seabed 24

25 Lorient november 2017 (2) Algorithms results : line hour y (true) D (est) x (est) y (est) m (est) 2 12h23 2,12m 2,4m +0,9m +2,2m 12A.m2 0,6s 4 12h54 2,2m 2,35m +1,1m +2,05m 8A.m2 0,6s 6 14h24 1,95m 2,15m +1,2m +1,8m 10A.m2 0,6s t Survey geometry : Z X Z X Y Y 25

26 Conclusion Validation : detection/localization methods Target capabilities Necessity of good SNR for localization Noise reduction : Sensor calibration Attitude compensation Other noise sources (system) 26

27 Sources Anderson function methods JE Anderson. Magnetic airborne detection frequency responses. Naval Air Development Center, Aeronautical Electronic and Electrical Laboratory, Johnsville, PA, André Quinquis. Classification des signaux magnétiques TBF sous contrainte de décision. PhD thesis, Brest,

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