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.

Airlight-imposed errors for space-object polarimetric observations from the ground.

We discuss and characterize how polarimetric sensing is contaminated by various "airlight" phenomena, as well as unpolarized light from the target, when space objects are observed with a ground-based telescope. Estimates of the polarization state are limited by unpolarized target light regardless of sensor technology or estimator algorithm, and increased target brightness actually degrades estimation of the S1, S2, and S3 Stokes parameters if the added light is unpolarized. Unpolarized airlight in the field of view has an identical degrading effect. Atmospheric scattering can significantly polarize airlight, so airlight polarization must be calibrated and subtracted from the estimated target polarization. We derive an expression for the mean-square Stokes estimation error when noisy, biased estimates for the airlight polarization state are subtracted from noisy, biased estimates of the target polarization state; this expression shows that target and airlight Stokes estimation noise and bias generally sum in the ms estimation error for airlight-calibrated target Stokes. While SNR for the estimate of a given Stokes parameter increases with the magnitude of that parameter, estimation bias also appears to be correlated with magnitude. We note that when the linear Stokes reference is not arbitrary, requiring a rotational transformation of the estimated Stokes vector, the SNRs of the S1 and S2 estimates vary with the rotation angle. Finally, we show that measured data can be used in numerical calculations described here to approximate the errors associated with Stokes estimation, with or without airlight calibration.