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This paper discusses some of the techniques developed at MIT Lincoln Laboratory for information fusion of lidar-based biological standoff sensors, meteorology, point sensors, and potentially other information sources, for biodefense applications. The developed Spatiotemporal Coherence (STC) fusion approach includes phenomenology aspects and approximate uncertainty measures for information corroboration quantification. A supervised machine-learning approach was also developed. Computational experiments involved ground-truth data generated from measurements and by simulation techniques that were developed. The fusion results include performance measures that focus explicitly on the fusion algorithms' effectiveness. Both fusion approaches enable significant false-alarm reduction. Their respective advantages and tradeoffs are examined. |
