Abstract

A Kalman filter is applied to the k-space tomography imaging modality for state-space prediction of radio frequency (RF) sources. By seeding the Kaczmarz computed tomography algorithm with Kalman filtered predictions, spatial-spectral reconstruction of the RF environment is accelerated. This technique enables an N/M decrease in reconstruction time, where M is the standard number of iterations required to fully reconstruct the RF image and N is the number of iterations required when the algorithm is seeded with a correct Kalman filter input.

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