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. The data is fused by associating the Doppler differential from the RF sensors with the theoretical Doppler differential computed from the multi-spectral tracker output. The absolute difference and the root-mean-square difference are computed to associate the Doppler differentials from the two sensor systems. Performance of the algorithm was evaluated using synthetically generated datasets of an urban scene with multiple moving vehicles. The presented fusion algorithm correctly associates the cellular emanation with the corresponding video target for low measurement uncertainty and in the presence of favorable motion patterns. For nearly all objects the fusion algorithm has high confidence in associating the emanation with the correct multi-spectral target from the most probable background target.

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. We discuss and demonstrate techniques needed to quantify error for both area- and feature-based algorithms. We further show that such synthetic data sets can be used to quantify both the Fisher information and sample errors associated with estimation of the shift between images acquired in different spectral bands and, by extension, estimation of registration model parameters. With the flexibility offered by synthetic data, such characterization can be obtained for robust domains of image brightness, sensor parameters, and differences in image phenomenology.