Multi-spectral image shift-estimation error calculations using simulated phenomenology.

Registration of multi-spectral imagery is a critical pre-processing step for applications such as image fusion, but phenomenological differences between spectral bands can lead to significant estimation errors. To develop credible requirements for multi-spectral imaging systems, it is critical to characterize errors, both algorithmic and fundamental, associated with estimating registration parameters; however, attempting to quantify error using archival data sets poses a number of problems. In this paper, we demonstrate the use of commercially available graphics software and available optical property measurements to create fully synthetic, multi-spectral imagery with high-fidelity representations of emissive and reflective phenomenology.

Target Localization and Tracking by Fusing Doppler Differentials from Cellular Emanations with a Multi-Spectral Video Tracker.

We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused by the moving cellular emanation and then Doppler differentials between all sensor pairs are calculated. The multi-spectral video tracker uses a Gaussian mixture model to detect foreground targets and SIFT features to track targets through the video sequence.