This paper presents an algorithm for target detection and tracking by fusion of multispectral imagery. In all spectral bands, we build a background model of the pixel intensities using a Gaussian mixture model, and pixels not belonging to the model are classified as foreground pixels. Foreground pixels from the spectral bands are weighted and summed into a single foreground map and filtered to give the fused foreground map. Foreground pixels are grouped into target candidates and associated with targets from a tracking database by matching features from the scale-invariant feature transform. The performance of our algorithm was evaluated with a synthetically generated data set of visible, near-infrared, midwave infrared, and long-wave infrared video sequences. With a fused combination of the spectral bands, the proposed algorithm lowers the false alarm rate while maintaining high detection rates. All 12 vehicles were tracked throughout the sequence, with one instance of a lost track that was later recovered.

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Near the ground laser communication systems must operate in the presence strong atmospheric turbulence. To model the performance of a laser communication system operating in the real world we have developed an outdoor 3.2 km, partially over water, turbulence measurement and monitoring communication link. The transmitter side is equipped with the laser and the bank of 20 horizontally, in-line mounted light emitting diodes. The receiver side consists of two channels used for wavefront sensor and point spread function measurements. The effects of anisoplanatism on the point spread function and statistics of Fried parameter r 0 are discussed in this article.

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.

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