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• W2016004916 abstract "A new detection algorithm for lidar applications has been developed. The detection is based on hyperspectral anomaly detection that is implemented for time anomaly where the question is a target (aerosol cloud) present at range R within time t(1) to t(2) is addressed, and for range anomaly where the question is a target present at time t within ranges R(1) and R(2) is addressed. A detection score significantly different in magnitude from the detection scores for background measurements suggests that an anomaly (interpreted as the presence of a target signal in space/time) exists. The algorithm employs an option for a preprocessing stage where undesired oscillations and artifacts are filtered out with a low-rank orthogonal projection technique. The filtering technique adaptively removes the one over range-squared dependence of the background contribution of the lidar signal and also aids visualization of features in the data when the signal-to-noise ratio is low. A Gaussian-mixture probability model for two hypotheses (anomaly present or absent) is computed with an expectation-maximization algorithm to produce a detection threshold and probabilities of detection and false alarm. Results of the algorithm for CO(2) lidar measurements of bioaerosol clouds Bacillus atrophaeus (formerly known as Bacillus subtilis niger, BG) and Pantoea agglomerans, Pa (formerly known as Erwinia herbicola, Eh) are shown and discussed." @default.
• W2016004916 created "2016-06-24" @default.
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• W2016004916 date "2007-10-08" @default.
• W2016004916 modified "2023-10-16" @default.
• W2016004916 title "Lidar detection algorithm for time and range anomalies" @default.
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• W2016004916 doi "https://doi.org/10.1364/ao.46.007275" @default.
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