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Proceedings Paper

Extending decentralized Kalman filtering results for novel real-time multisensor image fusion and/or restoration
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Paper Abstract

We pursue the idea that recent 'decentralized' Kalman filter (KF) technology, by outfitting each participating imaging sensor with its own dedicated 2-D Kalman filter can be used as the basis of a sensor fusion methodology that allows a final collating filter to assemble the data from diverse imaging sensors of various resolutions into a single resulting image that combines all the available information (in analogy to what is already routinely done in multisensor Navigation applications). The novelty is in working out the theoretical details for 2-D filtering situations while assuming that the image registration problem has already been independently handled beforehand. We synchronize frame size and location of pixels of interest to be comparably located with same 'raster scan' speed and size used for each to match up for different sensors. Rule for linear Kalman filters with only Gaussian noises is that the combining of underlying measurements or sensor information can only help and never hurt. We interpret this approach as using several common views of the same scene, as instantaneously obtained from different sensors, all being stacked up vertically one on top of the other, each with its own local 2-D Kalman-like image restoration filter proceeding to raster scan (in multi-layer sync). Then apply the multi-filter combining rules from decentralized filtering to the bunch to obtain a single best estimate image as the resulting output as a convenient methodology to achieve sensor fusion.

Paper Details

Date Published: 14 June 1996
PDF: 17 pages
Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243196
Show Author Affiliations
Thomas Henderson Kerr, TeK Associates (United States)


Published in SPIE Proceedings Vol. 2755:
Signal Processing, Sensor Fusion, and Target Recognition V
Ivan Kadar; Vibeke Libby, Editor(s)

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