1. IntroductionToSensorML Alexandre Robin – October 2006

2. SensorML – Design Objectives Standard way of describing wide range of sensors and sensor systems (platforms, sensor grids…)  Electronic D atasheet Enable sensor discovery among high number of disparate sensors accessible through a network Integrate within OGC Sensor Web Enablement Framework Alexandre Robin - October 2006 Allow precise description of complex systems Keep simple case simple! (i.e. thermometer) Allow global cross-domain classification Allow local/specialized domain specific classification

3. SensorML – Design Objectives Alexandre Robin - October 2006 Describe precise lineage of data, with enough information to allow error propagation Facilitate data processing and geo-location  Automatic Provide enough information to understand and simulate sensor behavior With small human intervention in the general case Automatic processing within a specific domain/profile

4. SensorML – What can be described? Alexandre Robin - October 2006 Platforms and Constellations SML System Sensors & Models SML System SML Component Raw Data Nature Structure Encoding Data Processing SML ProcessModel SML ProcessChain Data Product Nature Structure Encoding

5. SensorML – What can be described? Alexandre Robin - October 2006 Frequency Response Geometry Characteristics - WHAT was measured? Phenomenology, Frequency Response - HOW was it measured? Calibration, Quality - WHERE was it measured? Geometry, Spatial Response & Sampling - WHEN was it measured? Temporal Sampling, Impulse Response - WHY was it measured? Application

6. SensorML – Sensor Systems Component 1 Thermometer System – Weather Station Component 2 Barometer Component 3 Anemometer Air Temperature Atmospheric Pressure Wind Speed Digital Number Component 4 Processing Digital Number Wind Chill Temp Alexandre Robin - October 2006

7. SensorML – Sensor Systems Alexandre Robin - October 2006 System – Aircraft Platform Ground Radation Aircraft Position Subsystem 1: Scanner Detector Band 1 Detector Band 3 Detector Band 2 Detector Band 4 Subsystem 2: INS GPS IMU Interleaved Scanline GPS Data Tuple IMU Data Tuple

8. SensorML – Header Info Alexandre Robin - October 2006 Keywords, Identifiers and Classifiers for classification and indexing in Registries and Catalogs Global Characteristics and Capabilities for quick view on System capabilities Relevant Contacts and Documents to point to additional knowledge and documentation Temporal, Legal and Security Constraints to make sure the document is used only when appropriate History to keep track of System changes such as calibration events or other modifications

9. SensorML – Inputs, Outputs, Connections Alexandre Robin - October 2006 Specify nature of measured phenomena. Points to dictionaries which provides robust cross-domain semantic associations Specify units of measure for each scalar component of the inputs and outputs Specify quality of values and constraints (interval, enumeration) Possibilities of grouping and defining arrays of values as input and output Define connections between components to describe their interactions within a System

10. SensorML – Relative Positions Platform GPS IMU Scanner Swath Alexandre Robin - October 2006 Relative positions of System components (Both location and orientation!) Reference Frames of System components (How it relates to hardware)

11. SensorML – Detector Component Identifiers Classifiers Constraints Detector Contacts Documentation References Characteristics Capabilities GeometryTiming Spatial Frame Temporal Frame Response Characteristics Additional information used for detail discovery and link to other documents Sensor internal geometry (look rays direction for a scanner or camera) Definition of coordinate frames attached to the sensor Identification and Classification terms for further discovery Sensor timing (look rays times for a scanner = gives time sequence) Response characteristics (calibration, error, frequency) Alexandre Robin - October 2006 Inputs Outputs Params

12. SensorML – Detector Response Alexandre Robin - October 2006 Calibration Curve Gives the mapping of input to output values for a steady state regime. Two curves are used to describe a Hysteretic behavior. Random Error Curve Gives the relative measurement error versus the input value itself or any other environmental quantity such as temperature. Spectral Response Curve Specifies dynamic characteristics of the detector in the frequency domain. It gives the sensitivity of the detector versus the frequency or wavelength of the input signal. Impulse Response Curve Specifies dynamic characteristics of the detector in the time domain. It represents the normalized output of the detector for an impulse (D function) input. Spatial Response Curve(s) Gives the sensitivity of the detector relative to spatial coordinates (location of the source, or orientation of the incoming signal, e.g., point spread function, polarization) Temporal Response Curve Gives the sensitivity of the detector relative to a temporal coordinate frame (e.g., sampling time). This is a more descriptive form of the integration time.

13. SensorML – Component Array Alexandre Robin - October 2006 Concept of SensorML Array can be used to describe arrays of any Component or System Powerful to describe large arrays of “almost” identical devices Ability to individually tweak elements of the array through an indexing mechanism

14. SensorML – Detector Array Alexandre Robin - October 2006

15. SensorML – Processing Chain IMU and GPS sensor data Look Up Table Scan Index TT Time Interpolator Scan Time + Look Ray Time Look Ray Position Adjusted Time INS Data LLA Point LLA To ECEF IFOI Geometry Ellipsoid Intersection Position in sensor CRS Position in ECEF CRS Position of INS in LLA Position of INS in ECEF Derived from relative positions of sensors Obtained from Sensor Geometry (FOV…) Obtained from Sensor Geometry and Timing Alexandre Robin - October 2006

16. SensorML – Data Description Alexandre Robin - October 2006 Scanline TimeDataArray … (x 720) Radiance Specify Data Structure (imagery, in-situ, spectral, …) Weather Data Time TemperaturePressureWind Speed Spectrum TimeDataArray … (x 250) Freq1Freq2Freq3

17. SensorML – Data Description Alexandre Robin - October 2006 Specify Data Structure (imagery) Image RGB (1024x768) DataArray … (x 768) DataArray … (x 1024) DataGroup GBR DataArray … (x 1024) DataGroup GBR

18. SensorML – Data Description Alexandre Robin - October 2006 Specify Data Encoding (ASCII, Base64 binary, Raw binary) Data structure can be described in the interface section of a System/Component Specify parameters for each scalar value in the structure Can specify compression methods and encryption Data structure can be described separately along with the observation values

19. Relevant Links Open Geospatial Consortium http://www.opengeospatial.org SensorML http://vast.uah.edu/SensorML Questions? Alexandre Robin - October 2006