Geo-registration and Geo-location Using Two Airborne Video Sensors

Geo-registration and geo-location of data collected by video sensors such as electro-optical and infrared cameras are two fundamental steps in the airborne surveillance of ground targets. With the availability of high-resolution imaging sensors and detailed mapping or terrain data sources, video dat...

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Bibliographic Details
Published inIEEE transactions on aerospace and electronic systems Vol. 56; no. 4; pp. 2910 - 2921
Main Authors Taghavi, Ehsan, Song, Dan, Tharmarasa, Ratnasingham, Kirubarajan, Thiagalingam, McDonald, Mike, Balaji, Bhashyam, Brown, Daly
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Airborne sensing
Airborne surveillance
Algorithms
Atmospheric modeling
Bias
Cameras
Computer simulation
Cost function
geo-registration
ground target tracking
Image resolution
Infrared cameras
Lower bounds
Mapping
multisensor fusion
Optical imaging
Registration
Sensor systems
Sensors
Surveillance
Surveillance systems
Unmanned aerial vehicles
Video data
video geo-location
video tracking
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Summary:Geo-registration and geo-location of data collected by video sensors such as electro-optical and infrared cameras are two fundamental steps in the airborne surveillance of ground targets. With the availability of high-resolution imaging sensors and detailed mapping or terrain data sources, video data plays an increasingly important role in modern surveillance platforms like unmanned aerial vehicles and airborne, ground, or maritime surveillance systems. Surveillance systems without any compensation for the inevitable sensor registration errors, i.e., biases, may make geo-location erroneous and render the surveillance platform less effective for precision targeting. This article deals with the modeling of sensor biases in geo-location and proposes a method to estimate them. The proposed method leads to a minimization problem with a nonlinear cost function. Detailed derivation of the bias model is given along with an algorithm to find the bias parameters. The achievable lower bounds for debiased geo-location are provided and simulations are used to demonstrate the validity of the proposed method.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2020.2995439