Sensor fusion

Sensor fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints).^[1]^[2]

The data sources for a fusion process are not specified to originate from identical sensors. One can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input.

Sensor fusion is also known as (multi-sensor) Data fusion and is a subset of information fusion.

Sensory fusion is simply defined as the unification of visual excitations from corresponding retinal images into a single visual perception a single visual image. Single vision is the hallmark of retinal correspondence Double vision is the hallmark of retinal disparity

Examples of sensors

Accelerometers
Electronic Support Measures (ESM)
Global Positioning System (GPS)
Infra-red / thermal imaging camera
Magnetic sensors
MEMS
Phased array
Radar
Seismic sensors
Sonar and other acoustic
Sonobuoys
TV cameras

→Additional List of Sensors

Sensor fusion algorithms

Sensor fusion is a term that covers a number of methods and algorithms, including:

Example sensor fusion calculations

Two example sensor fusion calculations are illustrated below.

Let ${{\textbf {x}}}_{1}$ and ${{\textbf {x}}}_{2}$ denote two sensor measurements with noise variances $\scriptstyle \sigma _{1}^{2}$ and $\scriptstyle \sigma _{2}^{2}$ , respectively. One way of obtaining a combined measurement ${{\textbf {x}}}_{3}$ is to apply the Central Limit Theorem, which is also employed within the Fraser-Potter fixed-interval smoother, namely ^[3] ^[4]

{{\textbf {x}}}_{3}=\scriptstyle \sigma _{3}^{{2}}(\scriptstyle \sigma _{1}^{{-2}}{{\textbf {x}}}_{1}+\scriptstyle \sigma _{2}^{{-2}}{{\textbf {x}}}_{2})

where $\scriptstyle \sigma _{3}^{{2}}=(\scriptstyle \sigma _{1}^{{-2}}+\scriptstyle \sigma _{2}^{{-2}})^{{-1}}$ is the variance of the combined estimate. It can be seen that the fused result is simply a linear combination of the two measurements weighted by their respective noise variances.

Another method to fuse two measurements is to use the optimal Kalman filter. Suppose that the data is generated by a first-order system and let ${{\textbf {P}}}_{k}$ denote the solution of the filter's Riccati equation. By applying Cramer's rule within the gain calculation it can be found that the filter gain is given by ^[4]

{{\textbf {L}}}_{k}={\begin{bmatrix}{\tfrac {\scriptstyle \sigma _{2}^{{2}}{{\textbf {P}}}_{k}}{\scriptstyle \sigma _{2}^{{2}}{{\textbf {P}}}_{k}+\scriptstyle \sigma _{1}^{{2}}{{\textbf {P}}}_{k}+\scriptstyle \sigma _{1}^{{2}}\scriptstyle \sigma _{2}^{{2}}}}&{\tfrac {\scriptstyle \sigma _{1}^{{2}}{{\textbf {P}}}_{k}}{\scriptstyle \sigma _{2}^{{2}}{{\textbf {P}}}_{k}+\scriptstyle \sigma _{1}^{{2}}{{\textbf {P}}}_{k}+\scriptstyle \sigma _{1}^{{2}}\scriptstyle \sigma _{2}^{{2}}}}\end{bmatrix}}.

By inspection, when the first measurement is noise free, the filter ignores the second measurement and vice versa. That is, the combined estimate is weighted by the quality of the measurements.

Centralized versus decentralized

In sensor fusion, centralized versus decentralized refers to where the fusion of the data occurs. In centralized fusion, the clients simply forward all of the data to a central location, and some entity at the central location is responsible for correlating and fusing the data. In decentralized, the clients take full responsibility for fusing the data. "In this case, every sensor or platform can be viewed as an intelligent asset having some degree of autonomy in decision-making."^[5]

Multiple combinations of centralized and decentralized systems exist.

Levels

There are several categories or levels of sensor fusion that are commonly used.* ^[6] ^[7] ^[8] ^[9] ^[10] ^[11]

Level 0 – Data alignment
Level 1 – Entity assessment (e.g. signal/feature/object).
- Tracking and object detection/recognition/identification
Level 2 – Situation assessment
Level 3 – Impact assessment
Level 4 – Process refinement (i.e. sensor management)
Level 5 – User refinement

Applications

One application of sensor fusion is GPS/INS, where Global Positioning System and Inertial Navigation System data is fused using various different methods, e.g. the Extended Kalman Filter. This is useful, for example, in determining the attitude of an aircraft using low-cost sensors.^[12] Another example is using the Data fusion approach to determine the traffic state (low traffic, traffic jam, medium flow) using road side collected acoustic, image and sensor data.^[13]

A practical example how to combine data of different displacement and position sensors in order to obtain high bandwidth at high resolution can be found in this master thesis.^[14] One can see the applied methods of optimal filtering (in sense of minimizing e.g. the energy norm) or the MIMO Kalman filter.

References

↑ Elmenreich, W. (2002). Sensor Fusion in Time-Triggered Systems, PhD Thesis (PDF). Vienna, Austria: Vienna University of Technology. p. 173.
↑ Haghighat, M. B. A., Aghagolzadeh, A., & Seyedarabi, H. (2011). Multi-focus image fusion for visual sensor networks in DCT domain. Computers & Electrical Engineering, 37(5), 789-797.
↑ Maybeck, S. (1982). Stochastic Models, Estimating, and Control. River Edge, NJ: Academic Press.
1 2 Einicke, G.A. (2012). Smoothing, Filtering and Prediction: Estimating the Past, Present and Future. Rijeka, Croatia: Intech. ISBN 978-953-307-752-9.
↑ N. Xiong; P. Svensson (2002). "Multi-sensor management for information fusion: issues and approaches". Information Fusion. p. 3(2):163–186.
↑ Rethinking JDL Data Fusion Levels
↑ Blasch, E., Plano, S. (2003) “Level 5: User Refinement to aid the Fusion Process”, Proceedings of the SPIE, Vol. 5099.
↑ J. Llinas; C. Bowman; G. Rogova; A. Steinberg; E. Waltz; F. White (2004). Revisiting the JDL data fusion model II. International Conference on Information Fusion. CiteSeerX 10.1.1.58.2996.
↑ Blasch, E. (2006) "Sensor, user, mission (SUM) resource management and their interaction with level 2/3 fusion" International Conference on Information Fusion.
↑ http://defensesystems.com/articles/2009/09/02/c4isr1-sensor-fusion.aspx
↑ Blasch, E., Steinberg, A., Das, S., Llinas, J., Chong, C.-Y., Kessler, O., Waltz, E., White, F. (2013) "Revisiting the JDL model for information Exploitation," International Conference on Information Fusion.
↑ Gross, Jason; Yu Gu; Matthew Rhudy; Srikanth Gururajan; Marcello Napolitano (July 2012). "Flight Test Evaluation of Sensor Fusion Algorithms for Attitude Estimation". IEEE Transactions on Aerospace and Electronic Systems. 48 (3): 2128–2139. doi:10.1109/TAES.2012.6237583.
↑ Joshi, V., Rajamani, N., Takayuki, K., Prathapaneni, N., Subramaniam, L. V., (2013). Information Fusion Based Learning for Frugal Traffic State Sensing. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence.
↑ Piri, Daniel (2014). "Sensor Fusion in Nanopositioning". Vienna, Austria: Vienna University of Technology. p. 140.

J. L. Crowley and Y. Demazeau Principles and Techniques for Sensor Data Fusion Signal Processing, Volume 32, Issues 1–2, May 1993, Pages 5–27

External links

International Society of Information Fusion

Sensors

Acoustic, sound, vibration	Geophone Hydrophone Microphone Seismometer

Automotive, transportation	Air–fuel ratio meter Blind spot monitor Crankshaft position sensor Curb feeler Defect detector Engine coolant temperature sensor Hall effect sensor MAP sensor Mass flow sensor Omniview technology Oxygen sensor Parking sensors Radar gun Speed sensor Speedometer Throttle position sensor Tire-pressure monitoring system Torque sensor Transmission fluid temperature sensor Turbine speed sensor Variable reluctance sensor Vehicle speed sensor Water sensor Wheel speed sensor

Chemical	Breathalyzer Carbon dioxide sensor Carbon monoxide detector Catalytic bead sensor Chemical field-effect transistor Electrochemical gas sensor Electrolyte–insulator–semiconductor sensor Electronic nose Fluorescent chloride sensors Holographic sensor Hydrocarbon dew point analyzer Hydrogen sensor Hydrogen sulfide sensor Infrared point sensor Ion selective electrode Microwave chemistry sensor Nitrogen oxide sensor Nondispersive infrared sensor Olfactometer Optode Oxygen sensor Pellistor pH glass electrode Potentiometric sensor Redox electrode Smoke detector Zinc oxide nanorod sensor

Electric, magnetic, radio	Current sensor Electroscope Galvanometer Hall effect sensor Hall probe Magnetic anomaly detector Magnetometer MEMS magnetic field sensor Metal detector Planar Hall sensor Radio direction finder Test light

Environment, weather, moisture	Actinometer Bedwetting alarm Ceilometer Dew warning Electrochemical gas sensor Fish counter Frequency domain sensor Gas detector Hook gauge evaporimeter Humistor Hygrometer Leaf sensor Psychrometer Pyranometer Pyrgeometer Rain gauge Rain sensor SNOTEL Snow gauge Soil moisture sensor Stream gauge Tide gauge Weather radar

Flow, fluid velocity	Air flow meter Anemometer Flow sensor Gas meter Mass flow sensor Water metering

Ionising radiation, subatomic particles	Bubble chamber Cloud chamber Geiger counter Neutron detection Particle detector Scintillation counter Scintillator Wire chamber

Navigation instruments	Airspeed indicator Machmeter Altimeter Attitude indicator Depth gauge Fluxgate compass Gyroscope Inertial navigation system Inertial reference unit Magnetic compass MHD sensor Ring laser gyroscope Turn coordinator Variometer Vibrating structure gyroscope Yaw-rate sensor

Position, angle, displacement	Accelerometer Auxanometer Capacitive displacement sensor Capacitive sensing Gravimeter Inclinometer Integrated circuit piezoelectric sensor Laser rangefinder Laser surface velocimeter Lidar Linear encoder Linear variable differential transformer Liquid capacitive inclinometers Odometer Photoelectric sensor Piezoelectric accelerometer Position sensor Rate sensor Rotary encoder Rotary variable differential transformer Selsyn Sudden Motion Sensor Tachometer Tilt sensor Ultrasonic thickness gauge Variable reluctance sensor Velocity receiver

Optical, light, imaging	Charge-coupled device Contact image sensor Electro-optical sensor Flame detector Infrared Kinetic inductance detector LED as light sensor Light-addressable potentiometric sensor Nichols radiometer Optical fiber Photodetector Photodiode Photoelectric sensor Photoionization detector Photomultiplier Photoresistor Photoswitch Phototransistor Phototube Position sensitive device Scintillometer Shack–Hartmann wavefront sensor Single-photon avalanche diode Superconducting nanowire single-photon detector Transition edge sensor Tristimulus colorimeter Visible-light photon counter Wavefront sensor

Pressure	Barograph Barometer Boost gauge Bourdon gauge Hot-filament ionization gauge Ionization gauge McLeod gauge Oscillating U-tube Permanent Downhole Gauge Piezometer Pirani gauge Pressure gauge Pressure sensor Tactile sensor Time pressure gauge

Force, density, level	Bhangmeter Force gauge Hydrometer Level sensor Load cell Magnetic level gauge Nuclear density gauge Piezoelectric sensor Strain gauge Torque sensor Viscometer

Thermal, heat, temperature	Bimetallic strip Bolometer Calorimeter Exhaust gas temperature gauge Flame detection Gardon gauge Golay cell Heat flux sensor Infrared thermometer Microbolometer Microwave radiometer Net radiometer Quartz thermometer Resistance thermometer Silicon bandgap temperature sensor Special sensor microwave/imager Thermistor Thermocouple Thermometer

Proximity, presence	Alarm sensor Doppler radar Motion detector Occupancy sensor Passive infrared sensor Proximity sensor Reed switch Stud finder Touch switch Triangulation sensor Wired glove

Sensor technology	Active pixel sensor Back-illuminated sensor Biochip Biosensor Capacitance probe Carbon paste electrode Catadioptric sensor Digital sensors Displacement receiver Electromechanical film Electro-optical sensor Fabry–Pérot interferometer Fisheries acoustics Image sensor Image sensor format Inductive sensor Intelligent sensor Lab-on-a-chip Leaf sensor Machine vision Microelectromechanical systems Photoelasticity Quantum sensor Radar Ground-penetrating radar Synthetic aperture radar Radar tracker Sensor array Sensor fusion Sensor grid Sensor node Soft sensor Sonar Staring array Transducer Ultrasonic sensor Video sensor technology Visual sensor network Wheatstone bridge Wireless sensor network

Related	List of sensors

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