Paper
17 April 2008 A simple algorithm for sensor fusion using spatial voting (unsupervised object grouping)
Holger M. Jaenisch, Nathaniel G. Albritton, James W. Handley, Randel B. Burnett, Robert W. Caspers, William P. Albritton Jr.
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Abstract
We present a simple algorithm for achieving unsupervised spatially distributed object fusion using spatial voting. We achieve spatial fusion of uncertain position estimates of disparate objects. These objects are portions of larger assemblies that cannot be directly observed by available sensors. Only the individual objects are discernable. The question arises how to fuse estimates of position uncertainty of these objects (potential assembly pieces) into an "assembly" whose location and extent can only be inferred. Our algorithm uses spatial correlation and stacking with voting of positional uncertainty ellipses. We present the positional algorithm and compare it to other methods of grouping uncertainties and find encouraging improved performance.
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Holger M. Jaenisch, Nathaniel G. Albritton, James W. Handley, Randel B. Burnett, Robert W. Caspers, and William P. Albritton Jr. "A simple algorithm for sensor fusion using spatial voting (unsupervised object grouping)", Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 696804 (17 April 2008); https://doi.org/10.1117/12.800944
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Cited by 6 scholarly publications.
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KEYWORDS
Sensors

Convolution

Monte Carlo methods

Data modeling

Sensor fusion

Evolutionary algorithms

Algorithm development

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