Enhanced Maritime Traffic Picture for the Canadian Arctic

Transcription

1 Enhanced Maritime Traffic Picture for the Canadian Arctic Giulia Battistello*, Martin Ulmke*, Camilla Mohrdieck** (*) Fraunhofer FKIE - Sensor Data and Information Fusion Department - Wachtberg, Germany (**) Airbus Defence and Space - Data Fusion Concepts, Integration & Tests Ulm, Germany 10 th SECURITY RESEARCH CONFERENCE Future Security Berlin, September 2015

2 PASSAGES PROJECT Protection and Advanced Surveillance System for the Arctic: Green, Efficient, Secure Canadian-German Partnership: July 2013 June 2016 To specify the requirements and the modular architecture of an innovative maritime system to support operations in Arctic waters with a focus on the Northwest Passage

3 Case Study: Covert Monitoring Scenario (I) Frobisher Bay & Hudson/Davis Straits Frobisher Bay Only waterway to Nunavut s capital Iqaluit 120km length, [20-40]km width with several islands Detailed charts not available Presence of ice Frobisher Bay Davis Strait Hudson and Davis Straits fishing grounds Illegal fishing activities border crossings

4 Case Study: Covert Monitoring Scenario (II) Frobisher Bay & Hudson/Davis Straits Problem: the compilation of maritime traffic picture i.e. the basis for safety and security applications is limited by the scarceness/absence of local sensors. Sensor data might be missing due to Sensor limited performance or unavailability Data transmission interruption Data blanking or spoofing Objective: Development of a monitoring service that guarantees an higher update rate of vessel tracks trough a minimal deployment of new sensors

5 Input Data Source SAT-AIS Satellite Automatic Identification System Latitude [deg] Time Window: Real Data from Exact Earth Cooperative Vessels Vessel ID, Position, Velocity, SOG, COG, Heading, Timestamp, Type and more Measurements come as bursts with inter-bursts of 90 mins Discontinuous tracks Longitude [deg] Fishing Passenger Cargo Ice Breaker Oil/Chemical Tanker Warship

6 Input Data Source SAT-SAR Images Satellite Synthetic Aperture Radar Real Data from RadarSAT-2 and TerraSAR-X Position, Heading, Length Low satellite revisit time for continuous surveillance False detections :44:02 UTC :13:02 UTC Validated with SAT-AIS Not validated

7 Input Data Source Passive Radar Innovation Simulated Data from network of passive sensors that exploit signals already present in the surveillance area as illuminators of opportunity GSM base stations and/or VHF radio stations Position, Velocity of moving vessels TX1 Advantages Reduced electro magnetic pollution Reduced installation and maintenance costs Not subject to authorization by safety authorities Covert tracking >> detection of non-collaborative vessels (not equipped with AIS or not using it) RX2 TX2 RX1

8 Passive Radar Data Simulation Set Up Analysis of maritime traffic through historical data Identification of available transmitters (Txs) Ground Truth Measurement Errors Optimization of the bistatic passive radar geometry (Tx-Rx) >> maximization of the target detection capability (PD) Sensors Definition AoI Sensors description AoI Sensors description Passive Radar Measurements Simulator Measurements Simulation of passive radar measurements and tracks Generation of Detection Probability Maps PD Maps Target Tracker Tracks

9 Sub-Areas Definition (I) 64 Area # Area #2 Latitude [deg] Area # Longitude [deg] Passenger Cargo Tanker TX 6 (4 real 2 new) FoV = 120 RX 1 GSM Field of View = 210 BW = 83.5KHz Angular Res = 3.2

10 Sub-Areas Definition (II) 64 Area # Area #2 Latitude [deg] Area # Longitude [deg] Passenger Cargo Tanker TX 6 (4 real 2 new) FoV = RX 1 GSM - 1 VHF Field of View = BW = 83.5KHz 15kHz Angular Res =

11 Sub-Areas Definition (III) 64 Area # Area #2 Latitude [deg] Area # Longitude [deg] Passenger Cargo Tanker TX 1 Omnidirectional RX 1 VHF Omnidirectional BW = 15kHz Angular Res = 10

12 Target Tracking Results (I) x Detection Probability Map Y [m] Lat [deg] Ground Truth PR Tracks X [m] x 10 5 Simulation Parameters Target RCS = 100 m2 Height = 20 m MDV = 4 m/s PFA = 10^ Lon [deg] Results 5 Targets in the AoI in the time window 42 track segments from Passive Radar

13 Target Tracking Results (II) 6.94 x 106 Detection Probability Map 62.6 Ground Truth PR Tracks Y [m] Lat [deg] X [m] x 10 5 Simulation Parameters Target RCS = 100 m2 Height = 20 m MDV = 4 m/s PFA = 10^ Lon [deg] Results 15 Targets in the AoI in the time window 33 track segments from Passive Radar

14 Target Tracking Results (III) x Detection Probability Map Ground Truth PR Tracks Y [m] X [m] x 10 5 Simulation Parameters Target RCS = 100 m2 Height = 20 m MDV = 4 m/s PFA = 10^-3 Lat [deg] Lon [deg] Results 15 Targets in the AoI in the time window 35 track segments from Passive Radar

15 Data Fusion Data Fusion step is required to process in a common framework the information from the heterogeneous data sources SAT-AIS Tracks Passive Radar Tracks DATA FUSION ENGINE SAT-SAR Detections Enhanced Maritime Traffic Picture

16 Data Fusion Data Fusion step is required to process in a common framework the information from the heterogeneous data sources SAT-AIS Tracks Passive Radar Tracks DATA FUSION ENGINE Not available for the selected regions Enhanced Maritime Traffic Picture

17 Data Fusion Results (I) Fusion of SAT-AIS tracks and Passive Radar tracks leads to higher update rate of vessel tracks >> vessel monitored for longer time window (red plots correspond to passive radar only observations) Track segmentation?

18 Data Fusion Results (III) Data fusion allows improving track continuity Lat [deg] Ground Truth PR Tracks Fused Tracks SAT-AIS Lon [deg] Target in AoI Visibility PR Tracking Output Data Fusion Output Segments 37 Associated to 5 tracks + 5 Not associated Segments 35 Associated to 10 tracks Lat [deg] Lon [deg]

19 Conclusions PASSAGES Project >> Enhanced Maritime Traffic Picture for the Canadian Arctic Covert Monitoring Scenario as case study >> to develop a monitoring service that guarantees an higher update rate of vessel tracks trough a minimal deployment of new sensors Satellite AIS tracks and SAT-SAR detections augmented by GSM/VHF based Passive Radar as innovative sensor Non cooperative vessels Fusion of heterogeneous sensor data leads to Higher update rate of vessel tracks Improved track continuity

20 Thank you for your attention! Dipl.-Ing. Giulia Battistello Department of Sensor Data and Information Fusion (SDF) Fraunhofer FKIE Webpage: passages.ie.dal.ca