Introduction To SensorML

Introduction Introduction

ToTo

SensorMLSensorML

Alexandre Robin – October 2006

SensorML – Design Objectives

Standard way of describing wide range of sensors and sensor systems (platforms, sensor grids…) Electronic Datasheet

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

SensorML – Design Objectives


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

SensorML – What can be described?


Platforms and Constellations

SML SystemSensors & Models

SML SystemSML Component

Raw Data

• Nature• Structure• Encoding

Data Processing

SML ProcessModelSML ProcessChain

Data Product

• Nature• Structure• Encoding

SensorML – What can be described?


Frequency Response

GeometryCharacteristics

- 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

SensorML – Sensor Systems

Component 1Thermometer

System – Weather Station

Component 2Barometer

Component 3Anemometer

AirTemperature

AtmosphericPressure

WindSpeed

Digital Number

Component 4Processing

Digital Number

Digital Number

WindChill

Temp


SensorML – Sensor Systems


System – Aircraft Platform

GroundRadation

AircraftPosition

Subsystem 1: Scanner

DetectorBand 1

DetectorBand 3

DetectorBand 2

DetectorBand 4

Subsystem 2: INS

GPS

IMU

InterleavedScanline

GPS Data Tuple

IMU Data Tuple

SensorML – Header Info


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

SensorML – Inputs, Outputs, Connections


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

SensorML – Relative Positions

Platform

GPSIMUScanner

Swath


Relative positions ofSystem components

(Both location and orientation!)

Reference Frames ofSystem components

(How it relates to hardware)

SensorML – Detector Component

IdentifiersClassifiersConstraints

Detector

ContactsDocumentationReferences

CharacteristicsCapabilities

GeometryTiming

Spatial FrameTemporal 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)


Inputs OutputsP

aram

s

SensorML – Detector Response


Calibration

in

out

Random Error

%

q

Spectral Response

dB

Impulse Response

t

dB

Spatial Response

Temporal Response

t

dB

Integrationtime

Calibration CurveGives the mapping of input to output values for a steady state regime. Two curves are used to describe a Hysteretic behavior.

Random Error CurveGives the relative measurement error versus the input value itself or any other environmental quantity such as temperature.

Spectral Response CurveSpecifies 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 CurveSpecifies 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 CurveGives 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.

SensorML – Component Array


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

SensorML – Detector Array


Sensor (CCD array)

Detector R1

R0

Radiance

Cell Index

DN [3000]

DN (16 bits)

Look Up Table

Table Data

Calibration

Spatial Sensitivity

Gain

Corrective Gain

Position (R1 vs. R0) (Sampling)

X Translation

Y Translation

Z Translation

X Rotation

Y Rotation

Z Rotation

Index

10º

-10º

Z

X

Y

R0

SensorML – Processing Chain

IMU and GPS sensor data

Look Up Table

Look Up Table

ScanIndex

T T

TimeInterpolator

ScanTime +

Look Ray Time

Look Ray Position

Adjusted Time

INSData

LLAPoint

LLA To ECEF

IFOIGeometry

EllipsoidIntersectionPosition in

sensor CRSPosition inECEF 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


SensorML – Data Description


Scanline

Time DataArray … (x 720)Radiance Radiance Radiance

Specify Data Structure (imagery, in-situ, spectral, …)

Weather Data

Time Temperature PressureWind Speed

Spectrum

Time DataArray … (x 250)Freq1 Freq2 Freq3



Specify Data Structure (imagery)

Image RGB (1024x768)

DataArray

… (x 768)

DataArray … (x 1024)DataGroup G BR

DataArray … (x 1024)DataGroup G BR



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

Relevant Links

Open Geospatial Consortium

http://www.opengeospatial.org

SensorML

http://vast.uah.edu/SensorML

Questions?


http://www.opengeospatial.org/

http://vast.uah.edu/SensorML

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