Abstract: The present invention is a multi-threat detection method and system for high-traffic areas, such as airports, other mass-transit hubs, malls, symposiums, and the like. The detection methodology comprises a set of detection blocks, each performing different functions, and taking the information resulting from each block and entering it into one or more detection algorithms. The blocks comprise microwave detectors, co-polarization versus cross-polarization processors, magnetometers, and cameras (optional). The values calculated by the blocks are entered into machine learning algorithms which detect a threat if a threshold value is met. The detection occurs at a distance and the alarm may be silent, such that authorities are able to address threats without the target being aware. The alarm is also able to differentiate among varying threats based on the values calculated. No shadowing problems exist, and thus several targets may be monitored simultaneously without slowing down the speed of processing.

Abstract: The present invention is a multi-threat detection method and system for high-traffic areas, such as airports, other mass-transit hubs, malls, symposiums, and the like. The detection methodology comprises a set of detection blocks, each performing different functions, and taking the information resulting from each block and entering it into one or more detection algorithms. The blocks comprise microwave detectors, co-polarization versus cross-polarization processors, magnetometers, and cameras (optional). The values calculated by the blocks are entered into machine learning algorithms which detect a threat if a threshold value is met. The detection occurs at a distance and the alarm may be silent, such that authorities are able to address threats without the target being aware. The alarm is also able to differentiate among varying threats based on the values calculated. No shadowing problems exist, and thus several targets may be monitored simultaneously without slowing down the speed of processing.