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

Thomas Henderson Kerr III

doi:10.1117/12.243196

14 June 1996 Extending decentralized Kalman filtering results for novel real-time multisensor image fusion and/or restoration

Thomas Henderson Kerr III

Author Affiliations +

Proceedings Volume 2755, Signal Processing, Sensor Fusion, and Target Recognition V; (1996) https://doi.org/10.1117/12.243196
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Thomas Henderson Kerr III "Extending decentralized Kalman filtering results for novel real-time multisensor image fusion and/or restoration", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); https://doi.org/10.1117/12.243196

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